Clonality of tumour cells

Clonality of tumour cells

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Does all cancer cells come from a single clone of cells or are there evidences of polyclonal cancer cells too?

Most tumors are probably not strictly clonal; all cancer cells in a tumor seldom have the exact same set of mutations. Tumors tend to be subclonal, with all cancer cells having a single common ancestor but with later evolution resulting in some mutations shared among all cancer cells but others restricted to sub-populations of cancer cells. This fact and its implications for therapy were noted 40 years ago by Nowell (Science 194:23-28, 1976), and verified by much recent work as reviewed for example by McGranahan and Swanton (Cancer Cell 27:15-26, 2015).

A tumor might need to have a billion ($10^9$) or so cancer cells to be clinically detectable. Thus many years and many cell divisions have passed since the time that a single cell developed the set of mutations originally needed to produce the invasive tumor. With a probability of some uncorrected mutation at each cell division, particularly in cancer cells that often have defects in DNA replication and repair and in chromosome separation, it is essentially impossible for all of the billion cancer cells in the tumor to have identical sets of mutations. See, for example, the work of Bozic et al (eLife 2: e000747,2013). So although there is a clonal origin of the tumor, the tumor is typically a collection of related but genetically distinct populations best considered to be subclones.

Differences among subclones present a major barrier to traditional cytotoxic therapies, to more modern targeted therapies, and to the immunotherapies that presently receive so much attention. Even if most of the tumor is sensitive and responds, a subclone whose mutations somehow provide resistance to the therapy will be able to repopulate the tumor thereafter. Bozic and Nowak (PNAS 111:15964-8, 2014) estimated that a clinically detectable tumor may have 10 or more subclones resistant to any targeted therapy, due to mutations accumulated during the subclinical growth of the tumor.

As examples from immunotherapy for cancer, McGranahan et al recently showed (Science 351:1463-1469, 2016) that responses to immune checkpoint inhibition in melanoma and non-small cell lung cancer were best in patients with clonal rather than subclonal tumor neoantigens for the immune system to attack. Zaretzky et al (New Engl J Med 2016, epub ahead of print) identified subclonal mutations in interferon-receptor signaling and antigen presentation in some patients, mutations enriched in the recurrent tumors following immune checkpoint inhibition and thus likely accounting for relapse following that therapy.

It is possible in principle for a single physical tumor to have multiple cells of origin. Tumors often develop from a region of tissue that has been subject to field cancerization, where carcinogenic challenges have produced many cells with mutations that lead to dysregulated but not invasive behavior. This can lead to multiple independent cancers arising from the same general location, as individual cells later develop the full complement of mutations needed for an invasive tumor. In principle, such multiple cancers might coalesce into a single tumor mass so that the tumor is polyclonal, but I'm not aware of any well documented cases. So it seems that tumors are seldom truly polyclonal with multiple independent cells of origin.

Deciphering clonality in aneuploid breast tumors using SNP array and sequencing data

Intra-tumor heterogeneity concerns the existence of genetically different subclones within the same tumor. Single sample quantification of heterogeneity relies on precise determination of chromosomal copy numbers throughout the genome, and an assessment of whether identified mutation variant allele fractions match clonal or subclonal copy numbers. We discuss these issues using data from SNP arrays, whole exome sequencing and pathologist purity estimates on several breast cancers characterized by ERBB2 amplification. We show that chromosomal copy numbers can only be estimated from SNP array signals or sequencing depths for subclonal tumor samples with simple subclonal architectures under certain assumptions.

Dendritic cell dynamics

Conventional dendritic cells (cDCs) are critical to innate immunity and orchestrating adaptive T cell responses. cDCs originate from a common precursor and can be delineated into different subtypes. Cabeza-Cabrerizo et al. use multicolor fate mapping in mice to show that precursor cDCs enter tissue, differentiate into a single subtype, and proliferate as clones of sister cDCs under steady-state conditions. Viral infection causes a rapid influx of cDCs into infected tissue, and these cells differentiate into tissue-resident cDCs and dilute preexisting cDC clones. These results provide insight into cDC dynamics in tissues and how infection can cause rapid changes in cDC population frequencies.

Single-cell atlas of tumor clonal evolution in liver cancer

Tumor evolution is a key feature of tumorigenesis and plays a pivotal role in driving intratumor heterogeneity, treatment failure and patients’ prognosis. Here we performed single-cell transcriptome profiling of 46 primary liver cancers from 37 patients enrolled for interventional studies. We surveyed the landscape of

57,000 malignant and non-malignant cells and determined tumor cell clonality by developing a machine learning-based consensus clustering method. We found evidence of tumor cell branching evolution using hierarchical clustering, RNA velocity as well as reverse graph embedding methods. Interestingly, an increasing tumor cell clonality was tightly linked to patients’ prognosis, accompanied by a polarized immune cell landscape. We identified osteopontin as a key player for tumor cell evolution and microenvironmental reprogramming. Our study offers insight into the collective behavior of tumor cell communities in liver cancer as well as potential drivers for tumor evolution in response to therapy.

The number and clonality of TCRs are associated with the prognosis of colorectal cancer

Credit: IDIBELL-Bellvitge Biomedical Research Institute

Colorectal cancer (CRC) is the third most common cancer in the world, with more than one and a half million new cases diagnosed annually. Approximately 20% of diagnosed stage II patients experience relapses after surgery. There is no marker yet that identifies stage II patients at risk of relapse. Therefore, it is important to be able to identify prognostic biomarkers for this specific setting.

To date, it was already known that the infiltration of T cells (a type of lymphocyte that is part of the adaptive immune system) plays an important role in the survival of patients with colorectal cancer. Thus, using a new technique called "TCR immuno-sequencing," in samples from more than 600 patients with colorectal cancer, they have verified the usefulness of this new biomarker. This new technique measures both the amount of infiltrated T lymphocytes and their clonality, which is the diversity of lymphocytes that recognize different targets.

This study is a collaboration led by Víctor Moreno, head of the Oncology Data Analysis Program (PADO) of the Catalan Institute of Oncology (ICO), the Bellvitge Biomedical Research Institute (IDIBELL), CIBERESP, and the University of Barcelona, and his team. The research group led by Dr. Steven Gruber, from the City of Hope National Medical Center in Los Angeles, USA, Dr. Harlan Robins, CEO of the Adaptive Biotechnologies company, spin-off of the Cold Hutch Cancer Research Center, Seattle, USA, and Prof. Gad Rennert, Carmel Medical Center and Technion, Haifa, Israel.

How has this study been carried out?

Using this new TCR immuno-sequencing technique, a total of 640 colorectal cancer tumors have been sequenced from four different studies, three from patients from ICO Hospitalet and one from Israel. Thus, unlike the other methods, by sequencing the TCR regions (receptor for lymphocytes that recognize tumor antigens), both, the abundance of the T cell that infiltrates to the tumor and the clonality index, were obtained.

The results obtained have shown that the combination of both variables, quantity and clonality, is associated with the prognosis. The samples with the highest amount of TCR and diversity of clones are those that show a better prognosis of the disease. According to the head of the Oncology Data Analysis Program (PADO) of the ICO-IDIBELL, Víctor Moreno, "the results of this study show that higher levels of TCR, and a greater diversity of TCR receptor, is associated with a better prognosis in this specific group of patients where there are still no clear markers of recurrence."


Division of Molecular Carcinogenesis, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands

Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, USA

Ludwig Institute for Cancer Research and University of Lausanne, Lausanne, Switzerland

Howard Hughes Medical Institute and Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Institute for Genomic Medicine at Nationwide Children’s Hospital, Ohio State University College of Medicine, Columbus, OH, USA

Howard Hughes Medical Institute and University of Texas Southwestern Medical Center, Dallas, TX, USA

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

Bloomberg-Kimmel Institute for Cancer Immunotherapy and Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Francis Crick Institute, London, UK

BIOPIC, Beijing Advanced Innovation Center for Genomics, School of Life Sciences, Peking University, Beijing, China

Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

Materials and methods

CLONET pipeline

A schematic view of the CLONET pipeline is shown in Figure S10A in Additional file 12. For this study input data were obtained as follows. Read counts of informative SNPs were extracted from BAM files using an in-house procedure, SCNAs were detected using SegSeq [39] from tumor and normal sequencing-based data, PM coordinates were as in original corresponding manuscripts, and REARRs were identified by means of dRanger and Breakpointer [14]. Finally, to avoid germline background effects, we filtered out genes (approximately 4,000) that intersect significant (size greater than 2 kb) known germline copy number variants [40].

Fluorescence in situhybridization validation of CLONET

To assess genomic deletion, disruptive translocations or polyploidy we used locus-specific dual-color FISH assays following a previously described approach [41],[42]. To assess subclonality, at least 200 nuclei per area were evaluated using a fluorescence microscope (Olympus BX51 Olympus Optical, Tokyo, Japan). The probes used for FISH assays were: SPRY2, 5′ RP11-51 N22 to 3′ RP11-478 F4 MSR1, 5′ RP11-6O24 to 3′ RP11-794E24 ERG, 5′ BAC RP11-372O17 to 3′ BAC RP11-24A11 reference probe on 10q25, BAC RP11-431P18).

CLONET on exome and targeted sequencing data

The local approach implemented in CLONET enables the analysis of samples with few SCNAs provided that informative SNP read counts and Log R values are available. As with WGS data, individual specific informative SNPs can be identified from matched normal DNA samples. Appropriate Log R values for exome genomic segments or for each targeted area can be obtained with platform-specific strategies and provided to CLONET as input. Specifically, in the case of exome data, array-based segmented data or SCNA segments directly inferred from exome data with recent well-performing tools [43] can be utilized. CLONET combines segment input with exome-derived read counts to estimate purity and ploidy and then nominate subclonal aberrations based on sequencing data. In the case of targeted sequencing data, copy number calls derived using custom control regions and very high coverage (>1,000X) [38] allowed for CLONET-based clonality estimation even in the case of low tumor content (<10%).

Expected distribution of the allelic fraction of a genomic segment

Consider a genomic segment that spans a set of informative SNPs for the individual of interest. For any such SNP with coverage cov, the total number of reads r supporting the reference base (reference reads) is the sum of the neutral reads (rn) and the active reads (ra) supporting the reference base. We define β as the ratio between neutral reads and the total number of reads spanning the SNP of interest. The probability of having k reference reads is then defined as the convolution of the probability of observing β*k neutral reads and (1 - β)*k active reads, that is:

We assume that P(r n = β*k) follows a binomial distribution with number of trials equal to β*cov and probability of success equal to ps (that is, the probability to observe a reference read). Note that ps may deviate from 0.5 due to read-mapping biases [44]. All the active reads either support the reference base or the alternative base as only one allele is represented by definition of active reads. We define N ref as the proportion of informative SNPs within the aberration that carry the SNP reference base in the allele represented by active reads (active allele). Then, the distribution P(r a = (1 ββ)*k) follows a categorical distribution with values equal to N ref if r a = (1 ββ)*k and equal to (1 - N ref) if r a = 0. Equation 2 can be written in a closed form as the sum of two binomial distributions (proof in Additional file 1):

where B(m|n,p) is the probability mass function of a binomial distribution, that is, the probability of m successes in n trials with success probability P.

Estimated proportion of neutral reads for a genomic segment

The unknown values β and N ref of Equation 3 can be inferred from the sequencing coverage at informative SNPs within the considered segment. In particular, given a segment Seg and a set I of informative SNPs within Seg, each informative SNP in I is a sample from the distribution of Equation 3. Optimization can be used to determine the values of β and N ref for each segment. Given a random pair (β, N ref), the Kolmogorov-Smirnov nonparametric goodness-of-fit test for discrete null distribution [45] computes the likelihood that the informative SNPs in I are a sample from P(r = k|cβN refps). Next, a particle swarm optimization method [13] finds a candidate pair β ^ , N ^ ref that best represents the distribution of the allelic fraction of the SNPs in I.

From neutral reads to non-aberrant reads

Consider a genomic segment Seg. If the Log R value of Seg supports a SCNA C, we define as aberrant those reads that cover Seg and are sequenced from cells harboring C. If Seg is a candidate somatic mono-allelic deletion, the percentage of neutral reads β corresponds to the percentage of reads that cover Seg and are sequenced from cells harboring both alleles, that is, neutral and non-aberrant reads correspond. If the Log R value of Seg supports a gain with integer copy number cn > 2, we have to re-scale β to obtain the percentage of sequenced cells that have copy number cn (that is, the percentage of non-aberrant reads). For the sake of simplicity, we reason in terms of at most one copy difference between alleles. If cn is odd, the number of neutral reads is the sum of the neutral reads from admixed cells plus the neutral reads of the gain (Figure S10B in Additional file 12). The percentage β cn of reads from cells with copy number cn is computed from the percentage of neutral reads β by removing neutral reads due to the gain, that is:

If cn is even and at most one copy difference between alleles is allowed, then β is close to one, as both alleles are equally represented. This reasoning applies to any arbitrary combinations of the number of alleles (Figure S10C in Additional file 12).

Test Description

Detection of monoclonal, polyclonal or oligoclonal populations of T-cells by PCR and fragment analysis of the TCRB and TCRG genes


A multimodality approach is used in the diagnosis of lymphomas. Diagnosis may be particularly challenging in some cases solely based on morphology and immunophenotyping. The molecular testing exploits the rearrangement of the T-cell receptor (TCR) gamma and T-cell receptor beta genes. These genes rearrange over kilobases of genetic sequence and are unique for each normal mature T-cell. A reactive process, thus will demonstrate a polyclonal expansion of T-cells with different sixe rearrangements whereas an oligoclonal process will produce a few clones and a malignant process will often demonstrate expansion of a T-cell population that originates from a single cell and hence all will have the same rearrangement. The detection of a clonal TCR gene rearrangement by polymerase chain reaction (PCR) can be extremely helpful as an aid in the diagnosis of clonal T-cell process.

Clinical Utility

If the nature of the T-cell lymphoproliferative process cannot be accurately determined by morphology and immunophenotyping, PCR-based studies for T cell receptor gene rearrangements can help in establishing clonality. The presence of a clonal T cell receptor gene rearrangement is usually (but not always) indicative of a neoplasmic process. This test can also be used to follow-up the patients post treatment to see if the clone still persists and compare the lesions from two different sites to determine if this is the same or a different clonal process.


T-cell clonality is determined by PCR and fragment analysis of fluorescently labeled products. Master mixes are commercially provided by InVivoScribe in a BIOMED-2 Assay for the TCRB and TCRG genes. Patterns consistent with polyclonal, oligoclonal and monoclonal T-cell populations are interpreted from fragment-sizing electropherograms. The tube B reaction for T gamma has been modified to provide more reliable detection of V11 rearrangements.

Specimen Requirements

  1. Peripheral Blood: 3-5 ml, collected in EDTA (purple top) tube, store at room temperature 24 hours
  2. Bone Marrow: 0.5-1 ml, collected in EDTA (purple top)tube, store at room temperature, 24 hours
  3. Tissue: Frozen or fresh tissue may be used. A minimum of 2 x 2 x 2 mm is required (5 x 5 x 5 mm is preferred). For local facilities, we prefer the sample to be sent fresh on wet ice to arrive in the lab the same day. For outside facilities, the preferred approach is to snap freeze tissue directly or as cell pellets at -20 or -70°C and mail with sufficient dry ice to prevent thawing before arrival at our laboratory.
  4. Paraffin Sections: 10 sections on glass slides. More sections may be required if the tissue is small. Please call the lab if you have questions. Include % tissue area involved by neoplasm and pathologist’s signature.
  5. Unacceptable Specimens: Decalcified specimen, Frozen blood or bone marrow specimens are unacceptable as are tissue samples that have undergone a freeze/thaw cycle(s).


Turnaround time

Within 3-7 business days of receipt

TCRB Gene 81340 TCRG Gene 81342

Shipment Must Include


  1. Cossman,J. and M. Uppenkamp, (1988), T-cell gene rearrangements and the diagnosis of T-cell neoplasms, in Classification, Diagnosis and Molecular Biology of Lymphoproliferative Disorders, Clinics in Lab Med., 8(1): 31-44.
  2. Benhattar, J. et al, (1995), Improved polymerase chain reaction detection of clonal T-cell lymphoid neoplasms, Diagn.Mol.Path., 4(2): 108-112.
  3. Coad,J. et al, (1997), Molecular assessment of clonality in lymphoproliferative disorders: II. T-cell receptor gene rearrangements, Mol.Diagn., 2 (1): 69-81.
  4. Vargas, R.L., R.E.Felgar, et al. 2008. Detection of clonality in lymphoproliferations using PCR of the antigen receptor genes: Does size matter? Leukemia Res. 32:: 335-338.
  5. van Dongen, JJM et al. 2003. Design and standardization of PCR primers and protocols for detection of clonal immunoglobulin and T-cell receptor gene recombinations in suspect lymphoproliferations: Report of the BIOMED-2 Concerted Action BMH4-CT98-3936, Leukemia 17: 2257–2317.
  6. Sandberg, Y et al. 2005. BIOMED-2 Multiplex Immunoglobulin/T-Cell Receptor Polymerase Chain Reaction Protocols Can Reliably Replace Southern Blot Analysis in Routine Clonality Diagnostics, J. Mol. Diag., 7:, 495.
  7. Langerak, A.W. et al. 2012. Euroclonality/BIOMED-2 guidelines for interpretation and reporting if G/TCR clonality testing in suspected lymphoproliferations. Leukemia 26: 2159-2171.
  8. Rothberg, P.G. et al. 2012. Clonal antigen receptor gene PCR products outside the expected size range. J. Hematopathol. 5: 57-67.

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Insights into the Fundamental Elements of Life

Our cell biology researchers collaborate with molecular, structural, genetic, developmental and evolutionary biologists as well as experts in genomics, genetics, virology, infectious disease, computational biology, pathology and clinical research.

Many Fred Hutch scientists seek a deeper understanding of fundamental cellular structures. Structures inside cells influence everything from individual cell movement to an organism’s metabolism. They include the cytoskeleton, a dynamic internal protein network that gives cells their shape and their ability to move — a key function that spreading cancer cells can turn to their advantage. Our researchers also study how cells package and organize their DNA, which influences gene expression, and how the shape of a nerve cell’s membrane affects communication between neurons.

Our investigators also study fundamental cellular processes that cancer cells often co-opt. For example, stem cells can either renew themselves or turn into specialized cells. Cancer cells can often acquire “stem-like” properties that allow them to grow unfettered. Our researchers study normal and cancer stem cells, as well as characteristics of early-developing organs that tumors can adopt. Other processes they study in the context of health and disease include how cells adhere to and communicate with one another and how they build proteins.

Clonality Inference from Single Tumor Samples Using Low-Coverage Sequence Data

Inference of intra-tumor heterogeneity can provide valuable insight into cancer evolution. Somatic mutations detected by sequencing can help estimate the purity of a tumor sample and reconstruct its subclonal composition. Although several methods have been developed to infer intra-tumor heterogeneity, the majority of these tools rely on variant allele frequencies as estimated via ultra-deep sequencing from multiple samples of the same tumor. In practice, obtaining sequencing data from a large number of samples per patient is only feasible in a few cancer types such as liquid tumors, or in rare cases involving solid tumors selected for research. We introduce CTPsingle, which aims at inferring the subclonal composition by using low-coverage sequencing data from a single tumor sample. We show that CTPsingle is able to infer the purity and the clonality of single-sample tumors with high accuracy, even restricted to a coverage depth of ∼30 × .

Keywords: DNA sequencing cancer progression intra-tumor heterogeneity.

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