Each day we see more relationships between genes, SNPs,
miRNA, and now CNVs to some form of cancer. There is a recent paper in The
American Journal of Pathology which relates CNVs to prostate cancer, PCa, and
the prognosis of the disease.
We start with a brief discussion of a CNV. It is defined as
follows:
Copy number variant (CNV): A duplication or deletion event involving >1 kb
of DNA.
Simply a CNV may be the addition
of one or more copies of a gene or part thereof in a chromosome. It simply adds
to the chromosome. They are quite common and thus are seen frequently. Some are
related to certain genetically inherited disorders. In the paper at point they
are used to ascertain potentially prognostic data.
From the paper by Yu et al[1]:
The prediction of prostate
cancer clinical outcome remains a major
challenge after the diagnosis, even with improved early detection by prostate-specific antigen (PSA) monitoring.
To evaluate whether copy number variation (CNV) of the
genomes in prostate cancer tumor, in benign prostate tissues adjacent to the tumor (AT),
and in the blood of patients with prostate
cancer predicts biochemical (PSA)
relapse and the kinetics of relapse, 241 samples (104 tumor, 49 matched AT, 85
matched blood, and 3 cell lines) were analyzed ...
By using gene-specific CNV from tumor, the genome model
correctly predicted 73% (receiver operating characteristic P = 0.003) cases for
relapse and 75% (P < 0.001) cases for short PSA doubling time (PSADT, <4 0.="0.">
By using median-sized CNV from tumor, the genome model
correctly predicted 75% (P < 0.001) cases for relapse and 80% (P < 0.001)
cases for short PSADT. For the first time, our analysis indicates that genomic
abnormalities in either benign or malignant tissues are predictive of the
clinical outcome of a malignancy.
We briefly examine the CNV in general. In the work of
Freeman et al we have[2]:
DNA copy number variation has long been associated with
specific chromosomal rearrangements and genomic disorders, but its ubiquity in
mammalian genomes was not fully realized until recently. Although our
understanding of the extent of this variation is still developing, it seems
likely that, at least in humans, copy number variants (CNVs) account for a
substantial amount of genetic variation. Since many CNVs include genes that
result in differential levels of gene expression, CNVs may account for a
significant proportion of normal phenotypic variation. Current efforts are
directed toward a more comprehensive cataloging and characterization of CNVs
that will provide the basis for determining how genomic diversity impacts
biological function, evolution, and common human diseases.
We show an example of a CNV below graphically.
Here we have depicted a gene, the multicolor object in a
chromosome and we have shown a CNV with an identical copy of the gene in the
same chromosome. The authors continue:
CNVs often occur in regions reported to contain, or be
flanked by, large homologous repeats or segmental duplications. Segmental
duplications could arise by tandem repetition of a DNA segment followed by
subsequent rearrangements that place the duplicated copies at different chromosomal
loci. Alternatively, segmental duplications could arise via a duplicative
transposition-like process: copying a genomic fragment while transposing it
from one location to another
It must be noted that these are identical duplications of
the genes, or segments thereof. If of a gene the segment can be transcribed as
easily as the original. This raises the question that the resulting translated
protein is at a potential multiple level of concentration, although this may
not necessarily be the case. They continue:
Large duplications and deletions have been known for some
time to be related to the presentation of specific genetic disorders,
presumably as a result of copy number changes involving dosage-sensitive
developmental genes. This has led to the establishment of genetic diagnostic
tests for certain, well-characterized microdeletion and microduplication
syndromes (e.g., Angelman syndrome, DiGeorge syndrome, Charcot-Marie-Tooth
disease, etc.).
If a de novo chromosomal aberration is recognized in a
patient with a constitutional genetic abnormality (i.e., follow-up studies fail
to reveal a similar chromosomal aberration in either of the two parents, and
non-paternity has been excluded) and the aberration is not one of the dozen or
so well-known common chromosomal polymorphisms (e.g., inversion on chromosome 9),
the aberration is assumed to be the cause of the clinically recognized abnormal
phenotype.
Finally the CNVs are not necessarily related to disorders.
Some have CNV but many CNV are not noticeable. They thus state:
CNVs that do not directly result in early onset, highly
penetrant genomic disorders may consequently be considered to be neutral in
function, but afterward shown to play a role in later onset genomic disorders
or common diseases. Analyses of the functional attributes of currently known
CNVs reveal a remarkable enrichment for genes that are relevant to
molecular–environmental interactions and influence our response to specific
environmental stimuli.
These include, but are not limited to, processes
involving drug detoxification (e.g., glutathione-S-transferase,
cytochrome P450 genes, and carboxylesterase gene families), immune response and
inflammation (e.g., leukocyte immunoglobulin-like receptor, defensin, and APOBEC
gene families), surface integrity (e.g., late epidermal cornified envelope and
mucin gene families), and surface antigens (e.g., galectin, melanoma antigen
gene, and rhesus blood group gene families). Likewise, some CNVs encompass
genes that may contribute to interindividual variation in drug responses, as
well as in immune defense and disease resistance/susceptibility among humans.
From the Thorne and District Gazette[3]:
This study was appropriately designed to see whether
patients who have different outcomes have differences in copy number variation.
However, before this technique can be used as a test, it will have to be
trialled on a much larger cohort of people, so that researchers can get a
clearer picture of its use in clinical settings. For example, researchers will
need to know how often the test might miss patients that are likely to relapse,
and also how often the test incorrectly suggests a person’s cancer is likely to
relapse, which could lead them to have unnecessary further treatment. Also, as
the authors note, the techniques used in this study need high-quality DNA, so
may be difficult and expensive to perform…
The article then states regarding the outcomes:
- Approximately one-third of the patients had a relapse soon after surgery, with a median time to progression of 1.9 months.
- One-third had a relapse but much more slowly, with a median time to progression of 47.4 months.
- One-third of patients in the cohort were free of cancer for at least five years.
Based on the associations they found, the researchers developed an algorithm for predicting whether a patient would relapse, and how quickly they would relapse. This was based on whether the genetic code at specific locations was repeated or deleted, or on the size of copy number variation found across a person’s genome. They then tested their prediction model on an additional 25 samples.
The researchers found that the prostate cancer samples
had a large number of genetic abnormalities. (i) Deletions of specific regions
occurred at high frequency, and amplification (abnormal repetitions) of other
regions occurred in a subset of samples. (ii) Healthy tissue adjacent to a
tumour also had similar amplification and deletion patterns. (iii) The blood of
patients with prostate cancer also contained copy number variations, and some
of these variations occurred in the same locations within the DNA as they had
in the prostate cancer samples.
The researchers then developed a tool to predict whether
a cancer would relapse based on DNA regions that had a significant proportion
of amplification or deletion in prostate tissue samples from patients who
relapsed, but not in patients who did not relapse. The prediction model looking
at cancer tissue samples could predict a relapse correctly 73% of the time. (i)
It had a 75% accuracy for predicting rapid relapse. (ii) The prediction model
based on examining healthy tissue samples could predict a relapse 67% of the
time. (iii) It had a 77% accuracy for predicting rapid relapse. (iv) This
blood-based prediction model had an accuracy of 81% for predicting relapse, and
a 69% accuracy for predicting rapid relapse. (v) The cancer tissue analysis
tool had an accuracy of 70% for predicting relapse, and 80% for rapid relapse. (vi)
The healthy tissue sample tool had an accuracy of 70% for relapse and rapid
relapse, and (vii) the blood sample tool had an accuracy of 100% for relapse
and 80% for rapid relapse.
This is but another way to examine PCa cells. It does pose
several questions:
1. Pathways: Is there also a set of pathway malfunctions
that one sees in PCa also present here?
2. Is the CNV an artifact or causative. If causative then
what is the specific process and how does it relate to known pathways.
3. This is a complex cellular measurement of genes. Is this
cost effective?
4. The classic issue of stem cells again is raised. What
chromosomes do we look at? Is this specific only to the PCa cells, the PCa stem
cells, and all cells?
Definitions from Freeman et al:
2. Copy number variant (CNV); A duplication or deletion event involving >1 kb of DNA.
3. Duplicon :A duplicated genomic segment >1 kb in length with >90% similarity between copies
4. Indel: Variation from insertion or deletion event involving <1 span="span" style="letter-spacing: .4pt;"> kb of DNA.
5. Intermediate-sized structural variant (ISV): A structural variant that is ∼8 kb to 40 kb in size. This can refer to a CNV or a balanced structural rearrangement (e.g., an inversion).
6. Low copy repeat (LCR): Similar to segmental duplication.
7. Multisite variant (MSV): Complex polymorphic variation that is neither a PSV nor a SNP.
8. Paralogous sequence variant (PSV): Sequence difference between duplicated copies (paralogs.)
9. Segmental duplication: Duplicated region ranging from 1 kb upward with a sequence identity of >90%. (Interchromosomal: Duplications distributed among nonhomologous chromosomes and Intrachromosomal: Duplications restricted to a single chromosome)
10. Single nucleotide polymorphism (SNP): Base substitution involving only a single nucleotide;∼10 million are thought to be present in the human genome at >1%, leading to an average of one SNP differenceper1250 bases between randomly chosen individuals
[1]
Yu, Y., et al, Genome Abnormalities Precede Prostate Cancer and Predict
Clinical Relapse, The American Journal of Pathology - June 2012 (Vol. 180,
Issue 6, Pages 2240-2248, DOI: 10.1016/j.ajpath.2012.03.008). http://www.journals.elsevierhealth.com/periodicals/ajpa/article/S0002-9440%2812%2900241-6/abstract
[2]
Freeman, J., Copy number variation: New insights
in genome diversity, June 29, 200610.1101/gr.3677206 Genome
Res. 2006. 16: 949-961
http://genome.cshlp.org/content/16/8/949.full.html#ref-list-1
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