Single-Patient Molecular Testing with NanoString nCounter Data Using a Reference-Based Strategy for Batch Effect Correction

<div><p>A major weakness in many high-throughput genomic studies is the lack of consideration of a clinical environment where one patient at a time must be evaluated. We examined generalizable and platform-specific sources of variation from NanoString gene expression data on both ovarian cancer and Hodgkin lymphoma patients. A reference-based strategy, applicable to single-patient molecular testing is proposed for batch effect correction. The proposed protocol improved performance in an established Hodgkin lymphoma classifier, reducing batch-to-batch misclassification while retaining accuracy and precision. We suggest this strategy may facilitate development of NanoString and similar molecular assays by accelerating prospective validation and clinical uptake of relevant diagnostics.</p></div

PVCA and PCA plots of the Hodgkin Lymphoma clinical samples.

<p>We considered the PVCA plot (A) of the HL clinical samples run in different batches. The percentages represent the variability explained by each factor and first order interaction between factors. The PCA plot (B) provides a two-dimensional summary of the pairwise plot of the first three principal components, which represent 49% of the variability in the data. HL1, HL2, and HL3 label each of unique CodeSets corresponding to the HL gene list.</p

Concordance between duplicates of HL clinical samples obtained from two CodeSets, after adjusting using different methods.

<p>Concordance between duplicates of HL clinical samples obtained from two CodeSets, after adjusting using different methods.</p

Percentage of genes detected as a function of Signal to Noise Ratio by cohort.

<p>Each point on the plot represents a NanoString nCounter unique run (duplicates and triplicates included where available). The zoomed in section illustrates how the selected cut-off excludes samples that have low signal to noise and low % genes detected. HL: Hodgkin lymphoma clinical samples, OC: ovarian cancer clinical samples, OVCL: ovarian cancer cell lines, HLO: oligonucleotides corresponding to the HL CodeSet, OVO: oligonucleotides corresponding to the OC CodeSet.</p

PVCA of the OC clinical samples after adjusting batch effect using different methods.

<p>We consider the PVCA plot of the OC clinical samples run in different batches after adjusting BE with different methods. In each plot, percentages represent the variability explained by each factor and first order interaction between factors.</p

Percentage of genes detected above the limit of detection (LOD) by cohort.

<p>Each point on the boxplot represents a NanoString nCounter unique run (duplicates and triplicates included where available). The colored boxes represent the distribution of the percentage of genes detected in a particular cohort. The white line indicates the median. A cutoff of 50% was used for Cell Lines and clinical samples, and 95% was used for oligonucleotide samples. HL: Hodgkin lymphoma clinical samples, OC: ovarian cancer clinical samples, OVCL: ovarian cancer cell lines, HLO: oligonucleotides corresponding to the HL CodeSet, OVO: oligonucleotides corresponding to the OC CodeSet.</p

PVCA and PCA plots of the ovarian cancer clinical samples.

<p>We considered the PVCA plot (A) of the OC clinical samples run in different batches. The percentages represent the variability explained by each factor and first order interaction between factors. The PCA plot (B) provides a two-dimensional summary of the pairwise plot of the first three principal components, which represent 40% of the variability in the data. CS1, CS2, and CS3 label each of unique CodeSets corresponding to the OC gene list.</p

PVCA of the HL clinical samples after adjusting batch effect using different methods.

<p>We consider the PVCA plot of the HL clinical samples run in different batches after adjusting BE with different methods. In each plot, percentages represent the variability explained by each factor and first order interaction between factors.</p