CAPP-Seq

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Population analysis is performed to identify recurrent mutations in a given cancer type. This is done by analyzing public data sets such as the COSMIC cancer database and TCGA. A ‘selector’ is designed which consists of biotinylated DNA oligonucleotide probes targeting the recurrently mutated regions chosen for the specific cancer type. The selector is chosen using a multiphase bioinformatics approach. Using the selector, a probe-based hybridization capture is performed on tumor and normal DNA to discover mutations specific to the patient. The hybridization capture is then also applied to ctDNA to quantify the mutations that were previously discovered.^[1]

Peripheral blood is collected from patients and ctDNA is isolated from ≥1 mL of plasma. Input DNA can be as low as 4 ng.^[1]

There were four main goals in adapting this protocol for ctDNA work:

• 1) to optimize the adapter ligation efficiency
• 2) to reduce the number of PCR cycles needed after ligation
• 3) to preserve the naturally occurring size distribution of ctDNA (median 170 base pairs)
• 4) to minimize the variability in depth of sequence coverage across all the captured regions

These were achieved by allowing adaptor ligation to be carried out at 16°C for 16 hours to increase adaptor ligation efficiency and recovery. The most important adaptation is during enzymatic and clean-up steps; they are performed with-bead, in order to minimize tube transfer steps which increases recovery.

In CAPP-Seq, design of selector is a crucial step that identifies recurrent mutations in a particular cancer type using publicly available next generation sequencing data. For inclusion in CAPP-seq selector, the recurrent mutations that are enriched in a population is described by an index- Recurrence Index (RI). RI is the number of mutations per kilobase of a given genomic locus of a patient carrying particular mutations. RI represents a patient level recurrence frequency estimated for somatic mutations and all mutations. Known and driver recurrent mutations in a population can be ranked based on the RI and therefore RI is used to design a selector. A six phase design strategy is employed to design selector.^[1]

• Phase-1: Identifying frequently mutated known driver mutations using the publicly available data.
• Phase-2: Maximum coverage of SNVs among the patients was identified by ranking their exonic RI.
• Phase-3 and 4: Exons with higher RI were selected.
• Phase-5: Addition of previously predicted driver mutations.
• Phase-6: Addition of recurrent gene fusions rearrangement that are specific for particular cancer.

Human cancer is heterogeneous and recurrent cancer mutations are present only a minority of patient. Therefore, a careful and non-redundant design of selector is the vital part in CAPP-Seq and also the size of the selector is related to its downstream costs.

Hybridization capture with the selector probe set is performed on tumor DNA from a biopsy and sequenced to a depth of ~10,000× coverage. The biotinylated selector probes bind selectively to the regions of the DNA library that were chosen to be where the recurrently mutations occur in the given cancer type. In this way you are left with a smaller library that is enriched for only the regions you want, which can then be sequenced. This allows the determination of patient specific mutations. Hybridization capture with the same selector is then performed on ctDNA from the blood to quantify the previously identified mutations in the patient. CAPP-Seq can be applied to ctDNA from multiple blood samples at different time points in order to follow tumor evolution.

A series of steps are involved in analysis of CAPP-Seq data from mutation detection to validation and open source software can do most of the analysis. After the first step of variant calling, germline and loss of heterozygosity (LOH) mutations are removed in CAPP-seq to reduce the background biases. Several statistical significance tests can be performed against background to all type of variant calling. For example, statistical significance of tumor-derived SNVs can be estimated by random sampling of background alleles using Monte Carlo method. For the indel calls, statistical significance is calculated applying a separate method that used a strand specific analysis by Z-test shown in previous work.^[1] Finally, a computational validation steps reduces the false positive calls. However, a robust computational framework specific for CAPP-seq data analysis is a high demand in this field.

Sensitivity of this technology depends on the effective design of selector and highly biased with the size of the cohort and type of cancer under study. The lack of background to find the statistically significant recurrent variants has limited its performance due to stochastic noise and biological variability. Receiver operating characteristic (ROC) analysis on several cancer patient and cancer cured patient (sample collected at different tumor stages, circulating DNA time point, treatment, etc.) showed that CAPP-seq has higher sensitivity and specificity compared to previous methods in non–small-cell lung cancer.^[1]

The detection limit of CAPP-Seq is affected by three main areas: the input amount of ctDNA molecules, sample cross-contamination, potential allelic bias in the capture reagent, and PCR or sequencing errors. ctDNA is able to be detected at a lower limit of 0.025% fractional abundance in the blood. Sample cross-contamination was found to be a very small contribution and reports have shown minimal allelic bias towards capture of reference alleles in peripheral blood lymphocytes (PBLs). PCR and sequencing errors are also minimal.^[1] The technique becomes questionable when ctDNA is present at low levels of 0.01%. Also, when there is less discharge of ctDNA due to stability of tumor growth by therapy, the detection is compromised.^[12][13]

Whether ctDNA is released at equal or unequal rate from primary tumors and metastatic diseases is still unknown. This fact should be taken into consideration while performing CAPP-Seq as it can cause problems in determining tumor burden and clonal evolution if different tumors or clones are dying off and releasing their DNA at different rates. It is also unknown how tumor histology affects ctDNA release.^[1]

Another major limitation with using only ctDNA levels to detect tumor burden is that ctDNA can only predict residual tumor, it can tell nothing about the location of the tumor. This means that CAPP-Seq can be best used in complementary with other sequencing approaches^[10] for imaging disease burden at different times. Thus, technical sensitivity, reproducibility, specificity and requirement of expertise for analysis of large amount of data are some of the concerned issues with the technique.