Wednesday, August 15, 2012

CNV and Prostate Cancer

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:

  1. Approximately one-third of the patients had a relapse soon after surgery, with a median time to progression of 1.9 months.
  2. One-third had a relapse but much more slowly, with a median time to progression of 47.4 months.
  3. 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.

 They then conclude:

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:

1.     Structural variant: A genomic alteration (e.g., a CNV, an inversion) that involves segments of DNA >1 kb.

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).

 [2] Freeman, J., Copy number variation: New insights in genome diversity, June 29, 200610.1101/gr.3677206 Genome Res. 2006. 16: 949-961