Genomic instability of cancer cells in h&e image

Genomic instability of cancer cells in h&e image

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Is there are any books or papers on the topic of visual differences(analyzing H&E microscopy) of high grade cancer cells vs low grade cancer cells vs non-cancer cells?

In particular, I am trying to measure how much of genome was altered by analyzing images.

Histologic diagnosis of tumors is made using the so-called WHO blue books.

Different tumors show different patterns of cellular and tissual aberrations.

On h&e karyorrexis and pyknosis are examples histological markers of nuclear aberration.

A recent paper reviews the salient visual and subvisual morphological changes of cancer nuclei and their possible cause or etiology and significance.

Cancer models, genomic instability and somatic cellular Darwinian evolution

The biology of cancer is critically reviewed and evidence adduced that its development can be modelled as a somatic cellular Darwinian evolutionary process. The evidence for involvement of genomic instability (GI) is also reviewed. A variety of quasi-mechanistic models of carcinogenesis are reviewed, all based on this somatic Darwinian evolutionary hypothesis in particular, the multi-stage model of Armitage and Doll (Br. J. Cancer 1954:81-12), the two-mutation model of Moolgavkar, Venzon, and Knudson (MVK) (Math. Biosci. 1979:4755-77), the generalized MVK model of Little (Biometrics 1995:511278-1291) and various generalizations of these incorporating effects of GI (Little and Wright Math. Biosci. 2003:183111-134 Little et al. J. Theoret. Biol. 2008:254229-238).


This article was reviewed by RA Gatenby and M Kimmel.


Cancer derives from aberrantly proliferating cells that can avoid cell death and persist despite an accumulation of genetic mutations. After initial malignant transformation, cancer cells continue to evolve during disease progression and tumor relapse (Alexandrov et al. 2020 Lipinski et al. 2016).

As a committed champion of evolutionism, Dr. Peter Nowell proposed the cancer clonal evolution theory, whereby cancer clones with a survival advantage can prevail under selection pressure (Nowell 1976). Similar to the classical species evolution model, cancer evolution is a dynamic process: various genotypic and phenotypic cellular changes can occur to confer a high degree of cell plasticity (Boumahdi and de Sauvage 2020 Yuan et al. 2019). As a main inducer of cancer heterogeneity and drug resistance, cell plasticity is driven by diverse causes, such as exogenous selection pressures (including environmental and treatment stress) and endogenous adaptability (including genetic and epigenetic instability) (Boumahdi and de Sauvage 2020 Yuan et al. 2019). Both oncogenes and tumor suppressors can affect cellular plasticity and adaptability. Data derived from next-generation sequencing approaches have implicated that cancer cell plasticity could be driven by numerous genetic changes during cancer evolution (Alexandrov et al. 2020 Ferrando and Lopez-Otin 2017) and revealed significant intra-tumoral heterogeneity in cancer cells. What’s more, genomic instability seems to not only change cellular phenotypes, but also alter cancer cell adaptability and plasticity in response to environmental changes and treatment pressures. In this review, we highlight the emerging connection between cancer cell plasticity, genomic instability and mutagenesis during cancer evolution, and offer our insight and opinion on direction and therapeutic strategies in this rapidly progressing research field.

Genomic instability of cancer cells in h&e image - Biology

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Cell Line Single CTC Identification, Isolation, WGA, and Sequencing

LNCaP, PC3, and VCaP cell lines were selected as representative prostate cancer cell lines with known genomic aberrations. LNCaP cells are characterized by a heterozygous PTEN deletion, VCaP cells harbor an amplification of the androgen receptor (AR) on the X chromosome, and PC3 cells have a homozygous PTEN deletion [37]. Cells were imaged using 4 channels: DAPI, CK, AR, and CD45. The presence of CK expression and lack of CD45 expression combined with intact DAPI meets the standard definition of CTC and is consistent with their epithelial origin [23,38]. The lack of AR protein in PC3 cells, despite an intact AR gene, is consistent with previously published studies [37,39]. The very high levels of AR protein in VCaP cells is consistent with a previously published AR gene amplification [40]. Representative immunofluorescent images from LNCaP, PC3, and VCaP cell lines stained with CK, AR, CD45, and DAPI are shown (Fig 2A–2C).

Representative CTC images from cell lines (A) LNCaP, (B) PC3, and (C) VCaP. Slides were stained with CK, AR, CD45, and DAPI. Individual CTCs were identified by Epic Sciences’ algorithm and visually confirmed.

Eight LNCaP cells, 8 PC3 cells, 5 VCaP cells, and 4 WBCs were individually isolated for WGA and sequencing library preparation. The mean WGA yield was 578 ng (n = 25, with a range of 227–1190 ng) (Fig 3A). The mean library yield was 581 ng (n = 23, with a range of 397–1216 ng) (Fig 3B). While 100% of the single cells isolated had sufficient WGA DNA concentrations, 23/25 (92%) of the NGS libraries passed QC with adequate yield and were further processed for sequencing. An average of 17 million reads/sample (n = 23, with a range of 13–22 million) were obtained. 99% of the reads mapped to the reference genome (hg38) with 79% of the reads mapping with a MAPQ score greater than 30 (Fig 3C).

(A) DNA concentrations and total yield for each single cell WGA as measured by UV/Vis. Libraries were constructed from independent replicates of single cells: 8 from LNCaP, 8 from PC3, 5 from VCaP, and 4 from WBCs. Overall, we achieved an average 100% success rate during the single cell whole genome amplification procedure: 8/8 single cells (100%) successfully amplified for PC3 and LNCaP cancer cell lines, while 5/5 (100%) single VCaP cells and 4/4 (100%) single WBCs amplified. An average yield of 578 ng (range of 227–1190 ng) was obtained from the single cell WGA reactions. (B) Concentrations of the next-generation sequencing libraries as measured by PicoGreen. All of the NGS libraries passed QC with adequate yield except for two samples that failed to render any detectable amount of library DNA product (one PC3 and WBC replica samples) (23/25 92% success rate). An average yield of 581 ng (range of 397–1216 ng) was obtained among the single cell NGS libraries. (C) Assessment of NGS read quality. >99% of the NGS reads had an average PHRED score greater than Q30 pre-alignment, indicating high quality reads. An average of 17 million reads/sample (in each direction) were obtained. 99% of the reads mapped to the reference genome (hg38) with 79% of the reads mapping with a MAPQ score greater than 30. *Failed library preparation.

Single Cell Sequencing Reproducibility in Cell Lines

Whole genome CNV profiles from single cells of LNCaP, PC3, VCaP and WBCs were log2 normalized to visualize areas of amplification or deletion. The copy number profiles from each of the independent biological replicates from LNCaP, PC3, and VCaP cell lines (S1A–S1C Fig) demonstrate the reproducibility of the assay in that consistent CNVs are detected in all biological replicates within a cell line. Representative copy number profiles from each cell line and the WBC control are shown in Fig 4A–4D. Correlation coefficients across replicates of LNCaP, PC3, VCaP and WBCs were used to estimate the copy number analysis reproducibility (Fig 4E). Absolute Pearson correlation values from 0–100% within and between cell lines are represented as a circular diagram using Circos Table Viewer ( Each segment is color coded, denoting a cell line replicate. Links connecting each segment are represented as ribbons, the width of which corresponds proportionally to the degree of correlation. Intra-cell line replicates show thick ribbons connecting with each other, whereas inter-cell lines show thin ribbons, indicating that the assay is highly reproducible within each cell line. To further assess the reproducibility of our single cell sequencing, we analyzed single cells, pools of 5 cells, and pools of 10 cells, in replicates of 5 each for the LNCaP, PC3 and VCaP cell lines. Cells were pooled prior to WGA and analyzed by NGS. Replicate samples were combined for further analysis by calculating the median value of normalized copy number for each gene. The Pearson correlation coefficients were calculated for every pair of samples. The correlation analysis indicated that regardless of the number of cells sequenced, CNV profiles for all replicates correlated highly within each cell line, but not across cell lines (S1 Table). These data also indicate that copy number variations that were observed for each cell line were reproducible by our NGS methods, supporting our method of determining CNVs and genomic instability in a single cell. Furthermore, to determine the rate of false positives called by our assay, we analyzed the incidence of private CNV events as calculated by concordance analysis. The incidence of private CNV events, which are CNV events found within a single cell replicate that are not present in other cells within the same cell line, was low within cell lines (S1 Table). These analyses indicate that there is high rate of intra-cell line concordance due to the low number of false CNV events called.

Whole genome copy number plots from prostate cancer cell lines (A) LNCaP, (B) PC3, and (C) VCaP, and (D) WBC controls. (E) Absolute Pearson correlation values (0–100%) were calculated across samples and viewed using Circos Table Viewer ( For visualization purposes, the top 25% highest correlations are displayed. Each color-coded segment represents a cell line replicate. Correlations between replicates are denoted by links or ribbons, the width of which is proportional to the degree of correlation. Much higher correlations were observed in intra-cell line comparisons than inter-cell line comparisons, indicating that the assay has good reproducibility regardless of cell line used. (F) Box-whisker plot of LST scores for prostate cancer cell lines and WBCs. All 3 cell lines had high LST scores compared to the WBCs, with PC3 and VCaP having the highest scores. (G) Box-whisker plot of log2 normalized DNA copy number in AR. Amplification of the AR gene was observed in the VCaP single cells reproducibly (5/5, 100%). This amplification was not observed in PC3 (0/7), LNCaP (0/8), or WBC controls (0/3). (H) Box-whisker plot of log2 normalized DNA copy number in PTEN. The VCaP cell line has non-deleted PTEN (0/5, 0%), while PTEN loss was detected in PC3 (6/7, 86%), LNCaP (1/8, 13%), and 1/3 WBC controls (1/3, 33%).

Cell Line Genomic Profiles and Instability Analysis

LST scores in the three tested prostate cancer cell lines and WBCs were determined (Fig 4F, Table 2). Significantly higher genomic instability signature scores were determined reproducibly for LNCaP, PC3, and VCaP, compared to WBC controls. These data are summarized in Table 2. Given the high mutation rates of these cell lines analyzed in bulk [41,42], our single cell LST analysis recapitulates these findings.

Cell Line CNV Reproducibility

The normalized AR DNA copy number change on chromosome X corresponding to the AR gene is consistent with a previously published AR gene amplification in VCaP cells [40] (Fig 4G) and with the high expression of the AR protein (Fig 2C). By CNV analysis, AR gene amplification was observed in 5/5 VCaP cells, but not in any other cell line analyzed or the WBC controls (Fig 4G).

The normalized PTEN DNA copy number change on chromosome 10 corresponding to the PTEN gene is consistent with the known PTEN status in these cell lines: null in PC3 and reduced in heterozygous LNCaP cells but 2 copies in VCaP cells [43–45] (Fig 4H). Based on our CNV cutoff (Z score > 3 for amplification, or < -3 for loss), 1/8 LNCaP, 6/7 PC3, and 0/5 VCaP cells were called as PTEN loss. 1/3 WBC cells are were called as PTEN loss. No cells analyzed in this group were observed to have a PTEN amplification. While we detected a reduction in normalized copy number for PTEN in the LNCaP cells, it did not reach statistical significance for deletion for most of the single cells analyzed. This is likely due to the multiploidy nature of this cell line, which may compress our sensitivity to identify heterozygous loss. In the 1 PC3 cells where PTEN loss was not observed, this was likely due to isolation of a normal WBC along with the PC3 cell of interest, leading to detection of the 2 copies of PTEN contributed by the contaminating WBC.

To address the possibility of detecting false CNVs due to the WGA process, we compared our NGS results to previously published Single Nucleotide Polymorphism (SNP) arrays on the LNCaP and PC3 cell lines [46]. We were able to recapitulate by NGS a subset of genes found to either be amplified or deleted by SNP array in both cell lines (S2 Table), indicating that our method can accurately reproduce known CNVs.

Minimal Sequencing Depth Requirement

We estimated the minimal amount of reads required for reliable determination of single cell genomic instability and other alterations in silico. Our CNV analysis pipeline was performed on down sampled (50%, 10%, 5% and 1% reads) single cell data from LNCaP, PC3, and VCaP cells. We observed consistent genomic instability scores detected with >

350K reads (S1D Fig), and less reliable genomic instability scores were observed with lower coverage. Given this finding, we are using 350K reads as the minimum number of reads cutoff in our QC.

Genomic Instability is Heterogeneous in Prostate Cancer CTCs

67 CTCs from 7 mCRPC cancer patients were sequenced, and the average number of CTCs sequenced per patient was 9.57 with a range of 2–17 CTCs per patient. Observed within this patient cohort is a wide range of CTC/mL counts, percentages of CK positive vs CK negative CTCs, and percentages of AR N-term positive and AR N-term negative CTCs, both within and between patients, reflecting the heterogeneous nature of the disease.

The distribution of LSTs was evaluated in patient CTCs. The majority of patients presented unstable CTC genomes except patients 4 and 5, which had few CTCs (2 and 3 CTCs, respectively) (Fig 5A, S3 Table). For patients with unstable genomes, heterogeneous LST scores were observed when analyzed at the single cell level, as summarized in S3 Table.

(A) Box-whisker plot of LST scores for patient CTCs. High LST scores were observed in 5/7 (71%) patients. (B) Dot plot of log2 normalized DNA copy number in AR. Amplification of the AR gene was observed in 5/7 (71%) patients. (C) Dot plot of AR N-Terminal protein expression status in each single CTC as detected by IF, 5/7 (71%) patients had amplified AR protein, where AR amplification is observed in both CK positive and CK negative CTCs. (D) Box-whisker plot of AR N-Terminal protein expression in AR copy number gain and copy number neutral CTCs. Higher AR protein expression was observed in the AR copy number gain group, p = 0.0025 by Student’s t-test. (E) Dot plot of log2 normalized DNA copy number in PTEN. Loss of PTEN was observed in 5/7 (71%) patients. In each figure, one dot represents a single CTC.

AR Copy Number Concordance in Prostate Cancer CTCs

AR DNA amplification was detected in 5 out of 7 patients (patient IDs 1, 2, 3, 6, 7) (Fig 5B), where a wide range of AR amplification was observed at the inter- and intra-patient level. Consistent with this finding, expression of AR protein (AR N-term) was detected in the same 5 out of 7 patients (Fig 5C). Significantly higher AR protein expression was observed within individual CTCs harboring AR copy number gain compared to AR neutral CTCs (p = 0.0025) (Fig 5D), suggesting AR gene amplification directly correlates to AR protein expression.

PTEN Copy Number Concordance in Prostate Cancer CTCs

PTEN loss was observed in 5 out of 7 patients (patient IDs 1, 2, 5, 6, 7) using CNV (Fig 5E, Table 3). To demonstrate concordance using an orthogonal method, single CTCs from the same patient cohort were analyzed for PTEN loss by FISH. Two patients have homozygous and one had hemizygous loss of PTEN confirmed by PTEN FISH, which correlated with NGS results (patient IDs 1, 6, and 2, respectively), whereas 2 patients were classified as non-deleted by both FISH and NGS (patient IDs 3 and 4) (Table 3). Representative FISH images from diploid and polyploid CTCs are shown in S2 Fig. Summaries of PTEN status as determined by FISH are shown in S3 Fig.

The Dual Roles of MYC in Genomic Instability and Cancer Chemoresistance

Cancer is associated with genomic instability and aging. Genomic instability stimulates tumorigenesis, whereas deregulation of oncogenes accelerates DNA replication and increases genomic instability. It is therefore reasonable to assume a positive feedback loop between genomic instability and oncogenic stress. Consistent with this premise, overexpression of the MYC transcription factor increases the phosphorylation of serine 139 in histone H2AX (member X of the core histone H2A family), which forms so-called γH2AX, the most widely recognized surrogate biomarker of double-stranded DNA breaks (DSBs). Paradoxically, oncogenic MYC can also promote the resistance of cancer cells to chemotherapeutic DNA-damaging agents such as cisplatin, clearly implying an antagonistic role of MYC in genomic instability. In this review, we summarize the underlying mechanisms of the conflicting functions of MYC in genomic instability and discuss when and how the oncoprotein exerts the contradictory roles in induction of DSBs and protection of cancer-cell genomes.

Keywords: MYC chemoresistance genomic instability γH2AX.


CTCs offer the opportunity to examine a patient’s tumor without the need for tissue biopsies. CTCs allow us to gain an understanding of the tumor cell genome: CTCs capture the nature of the whole tumor, and their molecular profiles reflect the dynamics of tumor cell evolution. It is anticipated that the molecular genetic profiling of CTCs will unravel the identification of those CTCs that exhibit a high level of GI linked to disease aggressiveness and progression. This approach will also detect those CTCs that are indolent with a low level of GI. The molecular characterization of CTCs may, in the future, enable truly personalized medicine for each patient.

Why is Genome Instability a Hallmark of Cancer and Aging?

One of my college biology professors once told his students that when a person lives long enough, he or she will eventually get cancer. He wasn’t wrong—older age is a major risk for developing cancer as more than 60 percent of cancer cases occur in people 65 years or older.

At first glance, aging and cancer seem like polar opposites. Aging is associated with senescence and death. Cancer is associated with uncontrolled growth. But there is a wealth of scientific evidence that leads scientists to believe aging and cancer have more in common that one might think.

What makes the elderly population more susceptible to cancer? What is the link between aging and cancer? To address these questions, we need to delve into one of hallmarks of cancer and aging: genome instability.

Genome instability is defined as an increased tendency of mutations to occur in your genome (defined as a complete set of your DNA). A cell does not suddenly have higher mutation frequency: genome instability is often a consequence of another mutation(s). A series of mutations must occur before the cell experiences full-blown genome instability.

Mutations arise from erroneous DNA replication

Mutations are consequences of errors made in DNA replication, a process that copies the DNA in order to prepare a cell for cell division. Before a cell divides, the DNA must be copied so the parent cell and the daughter cell have the equal amounts of identical DNA.

DNA is perhaps the most important material in a cell—it is a blueprint for the cell’s survival, maintenance, and reproduction. One can imagine how important it is for the copying process to be extremely accurate. An incorrect message can be harmful not only for the parent cell, but also for all of its descendants.

DNA replication is indeed extremely accurate, averaging only one mistake for every 10 9 to 10 10 copying events. On top of that, cells can detect and correct errors by proofreading the new product to make sure everything is correct. And as a cherry on top, the process is extremely efficient. In eukaryotic cells, there are 50 copying events per second on average. We are looking at a highly efficient machine that is more accurate than any man-made product.

But nothing is perfect—our cells would never get mutations if DNA replication were flawless. Spontaneous errors are made and can persist in the final DNA product as mutations when the errors somehow escape the cell’s surveillance system.

Mutagens increase mutations and chances of genome instability

You might think, “So mutations can spontaneously arise, but one mistake for every 10 9 to 10 10 events does not seem like much!” While that is true, our cells are constantly exposed to mutagens that damage the DNA and increase the chances of permanent error occurring.

Some of the mutagens are “endogenous,” meaning that they are byproducts of natural processes in the cell. There are two major classes of endogenous mutagens. First, spontaneous chemical reactions can cause errors. Second, byproducts of metabolism, such as reactive chemical molecules with oxygen, can damage the DNA. Unfortunately, there is not much we can do to avoid these.

Others are “exogenous”—these are the environmental mutagens that we can sometimes avoid. These include mutagens that are often heard about in the media, like UV light from the sun. The exogenous mutagens can increase DNA damage either by directly causing damages to the DNA or by increasing the amounts of endogenous mutagens.

Once a cell has more DNA damage because of mutagens, it becomes more susceptible to mutations in the DNA—including genes that are important for fixing DNA damages. Without a mechanism to properly fix the damages, the cell’s DNA becomes even more susceptible to mutations. In other words, the cell’s genome is now unstable.

Genome instability is a hallmark in both cancer and aging

Normal cells grow and die, much like people and other organisms. A cancer cell, on the other hand, is immortal because it can grow and divide indefinitely. In a cancer cell, the mechanism that stops the cell from uncontrolled growth is often mutated. So a cell with genome instability can transform into a cancer cell if genes important for stopping the uncontrolled growth get mutations and become non-functional. This is one of the reasons why genome instability is a hallmark of cancer.

In spite of the high accuracy, the endogenous and exogenous mutagens pose a great threat to the DNA. These mutagens are all around us. We are constantly exposed, and damages will accumulate over time. Naturally, an older organism will have dealt with more mutagens and is more likely to have DNA damages and errors than a younger organism. The fact that an older organism has more mutations than a younger organism is true not just for people, but for other organisms too, like budding yeast (used in baking and making beer).

Cells’ genomes can become really unstable when they age because mutations accumulate over time. In an older cell, genes that are important for fixing DNA damages and control the cell’s growth are more likely to have mutations than in a younger cell. The accumulation of mutations due to aging can also cause cells to lose control of their growth and become “cancerous.”

In an era of aging population

Although cancer and aging do have some common biological grounds, they are still very complex and are not completely understood. Researchers cannot over-generalize and say the lack of proper responses to DNA damages is what causes both aging and cancer. Is aging a direct consequence of the DNA damage itself or the genome instability that follows the damage? If DNA damage does cause both aging and cancer, why are the two consequences so different?

But what we do know is that aging is a risk for developing cancer. We live in an era of an aging population. People are living longer thanks to better sanitation and medical knowledge, and they are not having as many kids. The 65+-year-old population is the fastest growing segment of the U.S. population. Currently, 1 in 8 people in the States are older than 65. By 2030, 1 in 5 people of the U.S. population will be older than 65.

These changing U.S. demographics are creating new challenges for cancer care. First, the medical workforce may be too small to care for the increased number of cancer patients. Second, the cost of the cancer care is increasing rapidly, faster than other sectors of medicine. The cost of cancer care has increased from $72 million in 2004 to $125 million in 2010 and is projected to increase to $173 million by 2020. As a result, the Centers for Medicare and Medicaid Services, which is the largest insurer for people over 65, is facing financial challenges.

Healthcare providers will need to anticipate the epidemic and address it in many different ways, including research, clinical practice, and education, to fill the gap of evidence-based practice for treating the elderly population with cancer. A greater focus on the elderly population with cancer will result in improved treatments that are necessary with the projected demographic growth.

IRX5 prompts genomic instability in colorectal cancer cells

The Iroquois homeobox gene 5 (IRX5), one of the members of the Iroquois homeobox family, has been identified to correlate with worse prognosis in many cancers, including colorectal cancer (CRC). In this study, upregulation of IRX5 revealed a great reduction in the proliferation of CRC colorectal cancer cell line SW480 and DLD-1, which was accompanied by G1/S arrest, increased expression in cyclin E1, P21, and P53 and a decrease in cyclin A2, B1, and D1. Furthermore, IRX5-mediated an increase expression of RH2A protein, the biomarker of DNA damage. Consequently, the SA-β-gal level is higher in IRX5-overexpression cells compared to control ones, which showed elevated DNA damage triggered cellular senescence. Recapitulating the above findings, IRX5 exhibited higher levels of genomic instability. IRX5 may be a perspective target for cancer therapy and it deserves further investigation.

Keywords: IRX5 Iroquois homeobox genes RH2A pathway colorectal cancer genomic instability.


The last 20 years have witnessed significant advances in our understanding of the molecular events that are triggered by threats to the genome integrity. Key players have been identified and genetic and biochemical characterization of these genes and their encoded protein products now identify them as true “guardians of the genome.” Dysregulation of these genes leads to unstable genomes. Excessive genomic instability usually leads to cell death, a factor in the utility of MLN4924 as an anticancer agent. However, because of the many redundant pathways for preventing overreplication or for repair, it is rare for mutations (or epigenetic silencing) affecting single pathways to create so much genomic instability as to cause cell death. Instead, inactivation of these single pathways simply increases the mutation burden without provoking cell death. The resulting heterogeneity in the genes of daughter cells allows the appearance of cells with growth or survival advantages, the driving force for cancer development. The same heterogeneity lies behind the resistance of cancers to many types of therapy. Thus, a major objective of our field should be to exploit known anomalies of repair and replication control in cancers to determine which redundant repair pathways should be therapeutically inhibited to produce “synthetic lethality” in the cancer cells. This will convert the mutation-generating machinery in the cancer into an Achilles heel by pushing the malignant cells selectively into extensive genomic instability and cell death.


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