Additional file 4: Table S3. of Evaluating somatic tumor mutation detection without matched normal samples

List of TCGA samples used. (XLSX 63 kb

Additional file 2: Table S2. of Evaluating somatic tumor mutation detection without matched normal samples

List of 1321 targeted genes. (XLS 83 kb

Additional file 3: Figure S1. of Evaluating somatic tumor mutation detection without matched normal samples

Large tumor dataset quality control metrics. A. Principal component analysis and B. loadings using sequencing metrics. Colors in A. represent the different tissue sites of origin. C. Ratio of sequence reads aligning to the X and Y chromosome and cutoffs used to infer gender. D. Histogram of average coverage over targeted bases (filtered, aligned reads). Figure S2: VQSR filtering effects on tumor-only mutation detection. A. Fraction of total putative TGS mutations falling in each GATK VQSR tranche (PASS being the most specific, SNPto100 being the least specific). B. Fraction of TGS mutations seen in COSMIC more than five times falling into each VQSR tranche. C. Fraction of total putative WES mutations falling in each GATK VQSR tranche (PASS being most specific, SNPto100 being least specific). D. Fraction of WES mutations seen in COSMIC more than five times falling into each VQSR tranche. Figure S3: Mutation counts after filtering with additional population databases. Boxplots showing numbers of mutations detected after filtering with KAVIAR, ExAC, or both (excluding AF ≥ 1%) in addition to 1000 Genomes and ESP. The rightmost columns show the minimal effect of filtering with KAVIAR and ExAC after the normal filter has been applied. A. TGS cohort, B. WES cohort. Median counts are indicated by the dark line in the middle of the box. The bottom and top of the box are the first and third quartiles, respectively. The whiskers represent the most extreme points within 1.5 times the interquartile range. The y-axes are in the log scale. Figure S4: Normal pool features affect the ability to remove variants. Boxplots showing the putative mutation counts after filtering with titrated sample counts in the normal pool for A. TGS cohort, B. WES cohort. Figure S5: Total nonref counts, precision, and recall with subsequent filters. Total nonref counts (left), precision compare to MuTect (middle), and recall compared to MuTect (right) for A. TGS and B. WES. All plots are in a linear scale. Figure S6: Precision-recall curve. Plot showing approximate precision vs recall for A. TGS and B. WES. Data point circles are area-proportional to the number of putative mutations at each filter level. Note the largest circle across the middle of the plots corresponds to precision = 0, recall = 1. Also note that data point circle sizes are scaled to fit, and the scaling factors are different for TGS and WES. The red line indicates performance of the random classifier based on positives (median number of MuTect call)/total positions (targeted bases). Figure S7: Overlap between mutation calls across four methods. Non-area-proportional Venn diagram showing median mutation counts across samples called by each combination of methods. The bold underlined values are the intersection of all the four methods. The underlined values are the counts unique to each method. A. TGS (TCC) cohort and B. WES (TCGA) cohort. Figure S8: Overlap between mutation calls across three matched tumor/normal methods. Non-area-proportional Venn diagram showing median mutation counts across samples called by each combination of methods. The bold underlined values are the intersection of all the three methods. The underlined values are the counts unique to each method. A. TGS (TCC) cohort and B. WES (TCGA) cohort. (PDF 945 kb

Lung Cancer Cohort Data

47 clinical data elements for a cohort of 874 patients with adenocarcinoma of the lung

Tolerance Associated Gene Expression following Allogeneic Hematopoietic Cell Transplantation

<div><p>Biologic markers of immune tolerance may facilitate tailoring of immune suppression duration after allogeneic hematopoietic cell transplantation (HCT). In a cross-sectional study, peripheral blood samples were obtained from tolerant (n = 15, median 38.5 months post-HCT) and non-tolerant (n = 17, median 39.5 post-HCT) HCT recipients and healthy control subjects (n = 10) for analysis of immune cell subsets and differential gene expression. There were no significant differences in immune subsets across groups. We identified 281 probe sets unique to the tolerant (TOL) group and 122 for non-tolerant (non-TOL). These were enriched for process networks including NK cell cytotoxicity, antigen presentation, lymphocyte proliferation, and cell cycle and apoptosis. Differential gene expression was enriched for CD56, CD66, and CD14 human lineage-specific gene expression. Differential expression of 20 probe sets between groups was sufficient to develop a classifier with > 90% accuracy, correctly classifying 14/15 TOL cases and 15/17 non-TOL cases. These data suggest that differential gene expression can be utilized to accurately classify tolerant patients following HCT. Prospective investigation of immune tolerance biologic markers is warranted.</p></div

Direction and magnitude of change in non-TOL group vs. TOL and control: Genes with increased expression in non-TOL group.

<p>*CTSS—cathepsin S; FCER1G—Fc fragment of IgE, high affinity I, receptor for; gamma polypeptide; FCGR1B—Fc fragment of IgG, high affinity Ib, receptor (CD64); CD93—CD93 molecule; CR1—complement component (3b/4b) receptor 1; TLR1—toll-like receptor 1; VSIG4—V-set and immunoglobulin domain containing 4; DUSP6—dual specificity phosphatase 6; MNDA—myeloid cell nuclear differentiation antigen; GAPT—GRB2-binding adaptor protein, transmembrane; FKBP1A—FK506 binding protein 1A, 12kDa; TNFSF13B—tumor necrosis factor (ligand) superfamily, member 13b (BAFF); CDKN2B—cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4); HHEX—hematopoietically expressed homeobox; RHOB—ras homolog gene family, member B; CARD16—caspase recruitment domain family, member 16; SOD2—superoxide dismutase 2, mitochondrial.</p

Receiver operating characteristic (ROC) plot for diagnostic accuracy of gene classifier.

<p>*ROC plot for diagnostic accuracy presents true positive rate vs. false positive rate (or sensitivity x 1-specificity) for gene expression-based phenotypic classifier of TOL and non-TOL patient groups. AUC 0.97 (95% CI 0.82–0.97).</p

Enriched cellular process networks shared between current experimental data and published tolerance-associated gene expression data in solid organ transplantation.

<p>*Cellular process networks are ranked in descending order based on p value for magnitude of enrichment to annotated networks using MetaCore by GeneGo software (for each process network, solid organ = published solid organ transplant data,[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117001#pone.0117001.ref002" target="_blank">2</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117001#pone.0117001.ref005" target="_blank">5</a>] and HCT = HCT experimental data.</p><p>Enriched cellular process networks shared between current experimental data and published tolerance-associated gene expression data in solid organ transplantation.</p

Comparison of immune cell subsets among TOL and non-TOL patients.

<p>*Numbers indicate proportion of examined PBMC with the identified phenotype.</p><p>*NK—natural killer cell; DC—dendritic cell; IEL—intra-epithelial lymphocyte; Treg—regulatory T cell; NKT—NKT cells; TOL—tolerant patients; non-TOL—non-tolerant patients</p><p>Comparison of immune cell subsets among TOL and non-TOL patients.</p

Comparison of patient, transplantation, and GVHD variables across tolerant and non-tolerant groups.

<p>*categorical data compared with Fisher’s exact test or Chi-square, continuous data utilized wilcoxon rank sum test</p><p>* AA—aplastic anemia; ALL—acute lymphoblastic leukemia; AML—acute myelogenous leukemia; CML—chronic myelogenous leukemia; FL—follicular lymphoma; HD—Hodgkin lymphoma; IMF—idiopathic myelofibrosis; MCL—mantle cell lymphoma; MDS—myelodysplastic syndrome; MM—multiple myeloma; MPD—myeloproliferative neoplasm; PBSC—peripheral blood stem cells; BM—bone marrow harvested stem cells; MMUD—mismatched unrelated donor; MRD—matched sibling donor; MUD—matched unrelated donor; HLA—human leukocyte antigen; CMV—cytomegalovirus; neg—negative; pos—positive; Bu—busulfan; Cy—cyclophosphamide; Flu—fludarabine; ATG—anti-thymocyte globulin; R—rituximab; BCNU—carmustine; VP16—etoposide; TBI—total body irradiation; pento—pentostatin; CSA—cyclosporine; TAC—tacrolimus; MMF—mycophenolate mofetil; MTX—methotrexate; aGVHD—acute graft vs. host disease; pred—prednisone; rapa—rapamycin (sirolimus); ECP—extra-corporeal photopheresis; cGVHD—chronic graft vs. host disease</p><p>Comparison of patient, transplantation, and GVHD variables across tolerant and non-tolerant groups.</p