Additional file 2 of Enabling multiplexed testing of pooled donor cells through whole-genome sequencing

Table S1–6. all supplementary tables. (XLSX 638 kb

Additional file 1: of Enabling multiplexed testing of pooled donor cells through whole-genome sequencing

Note S1–2. working example of the method and comparison of the method against PRISM. (PDF 278 kb

table_1_High Densities of Tumor-Associated Plasma Cells Predict Improved Prognosis in Triple Negative Breast Cancer.docx

<p>Breast cancer is the most common malignancy affecting women, but the heterogeneity of the condition is a significant obstacle to effective treatment. Triple negative breast cancers (TNBCs) do not express HER2 or the receptors for estrogen or progesterone, and so often have a poor prognosis. Tumor-infiltrating T cells have been well-characterized in TNBC, and increased numbers are associated with better outcomes; however, the potential roles of B cells and plasma cells have been large. Here, we conducted a retrospective correlative study on the expression of B cell/plasma cell-related genes, and the abundance and localization of B cells and plasma cells within TNBCs, and clinical outcome. We analyzed 269 TNBC samples and used immunohistochemistry to quantify tumor-infiltrating B cells and plasma cells, coupled with NanoString measurement of expression of immunoglobulin metagenes. Multivariate analysis revealed that patients bearing TNBCs with above-median densities of CD38⁺ plasma cells had significantly better disease-free survival (DFS) (HR = 0.44; 95% CI 0.26–0.77; p = 0.004) but not overall survival (OS), after adjusting for the effects of known prognostic factors. In contrast, TNBCs with higher immunoglobulin gene expression exhibited improved prognosis (OS p = 0.029 and DFS p = 0.005). The presence of B cells and plasma cells was positively correlated (p < 0.0001, R = 0.558), while immunoglobulin gene IGKC, IGHM, and IGHG1 mRNA expression correlated specifically with the density of CD38⁺ plasma cells (IGKC p < 0.0001, R = 0.647; IGHM p < 0.0001, R = 0.580; IGHG1 p < 0.0001, R = 0.655). Interestingly, after adjusting the multivariate analysis for the effect of intratumoral CD38⁺ plasma cell density, the expression levels of all three genes lost significant prognostic value, suggesting a biologically important role of plasma cells. Last but not least, the addition of intratumoral CD38⁺ plasma cell density to clinicopathological features significantly increased the prognostic value for both DFS (ΔLRχ² = 17.28, p = 1.71E−08) and OS (ΔLRχ² = 10.03, p = 6.32E−08), compared to clinicopathological features alone. The best combination was achieved by integrating intratumoral CD38⁺ plasma cell density and IGHG1 which conferred the best added prognostic value for DFS (ΔLRχ² = 27.38, p = 5.22E−10) and OS (ΔLRχ² = 21.29, p = 1.03E−08). Our results demonstrate that the role of plasma cells in TNBC warrants further study to elucidate the relationship between their infiltration of tumors and disease recurrence.</p

Distribution of doubletons as a function of the eigen-map.

<p>The first eigen-vector versus second eigen-vector for (A) Baylor and (B) Broad samples. Eigen-vectors are obtained by applying PCA to all common variants. For each individual, we count the number of doubletons. To indicate the relative number of doubletons per individual, points are color-coded as follows: black (bottom : fewest doubletons), blue (next 25), green (next 25), and orange (top 25: most doubletons) within the Baylor and Broad samples, respectively.</p

Counts of non-synonymous variants in Baylor and Broad before filtering.

<p>Note: Single: count of singletons; Double: count of doubletons; RVs: count of variants with MAF and not singletons or doubletons; LFVs: count of variants with MAF ; CVs: count of variants with MAF .</p

-log10(observed p-values) versus -log10(expected p-values) of SKAT and Burden test for Mega-analysis.

<p>Panel (A) shows SKAT p-values, Panel (B) shows burden test p-values. and 1.047, for mega SKAT and burden test, respectively.</p

PCA from common variants, low frequency variants, and both types of variants.

<p>Plotted are the first eigen-vector versus second eigen-vector for Broad samples. Eigen-vectors are obtained by applying PCA to all common variants that have no missingness (56,607 variants) (A), all low frequency variants that have no missingness (29,509 variants) (B), and both type of variants (C). The colors are obtained by clustering individuals based on their coordinates in panel (A) using model based clustering <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003443#pgen.1003443-Fraley1" target="_blank">[51]</a>.</p

Distribution of rare variants per gene in Baylor and Broad data sets after filtering.

<p>Minor allele counts (MAC) are restricted to variants with minor allele frequency . Panel (A), distribution of mean MAC per sample, averaged over all genes. Panel (B), in the Baylor samples, genes were binned based on the counts of rare variants (which range from 1 to 30); for each bin the vertical axis shows the distribution of counts (boxplot) from the same genes in the Broad samples. The red line indicates an equal count in Broad and Baylor.</p

Number of significant genes (and expected number) under different filters.

<p>Note: These analyses are restricted to the genes that have more than 15 minor alleles in the samples used in each study. MAC columns show the number of minor alleles called per sample, Ba: Baylor, Br: Broad. Filter PASS includes all variants that score a “Pass” based on GATK, Filter MISS: missingness , Filter DpBal: missingness , depth balance for Baylor, for Broad.</p

Distribution of the genomic control factor .

<p>By permuting case/control status 100 times the distribution of is obtained based on the 1000 largest genes. The red line shows the mean of the permutation distribution and the green line shows obtained from the data using (A) Broad SKAT p-values obtained without eigen-vectors; (B) Broad SKAT p-values, with common variants (CVs) eigen-vectors, (C) Broad SKAT p-values, with low frequency variants (LFVs) eigen-vectors; and (D) Broad SKAT p-values, with CVs plus LFVs eigen-vectors.</p