Supplementary Table S4: Somatic single nucleotide variants (SNVs) and insertion/deletions (indels). Supplementary Table S5: Frequencies of mutations found in ER-positive/HER2-negative male breast cancer vs subsets of female breast cancer from the TCGA study. Supplementary Table S6: MutSigCV analysis of significantly mutated genes in MaBCs.

PDF file containing the Supplementary Methods and Supplementary References.

Supplementary Methods detailing the methods employed for Copy number alteration (CNA) analysis, Sanger sequencing and supplementary references.

Supplementary Table S4: Pathway analysis of genes with mutations enriched in the metastasis or associated with loss of heterozygosity (LOH) in the metastasis using Ingenuity Pathway Analysis and g:Profiler.

Supplementary Fig. S3: Evolution of mutational signatures in treatment-naïve patients with de novo synchronous metastatic breast cancer. (A) Heatmap represents the similarity of the observed mutational signatures (blue, see color key) to those previously observed in human cancers (15, 36), separately for the trunk mutations (yellow), the mutations specific to the primary tumor (green) and the mutations specific to the metastatic lesion (pink). (B) Barplots illustrate the mutational signatures of the mutations specific to the primary tumor and the metastatic lesion of Cases 2, 4, 7 and 9. In each panel, the colored barplot illustrates each mutational signature according to the 96 substitution classification defined by the substitution classes (C>A, C>G, C>T, T>A, T>C and T>G bins) and the 5' and 3' sequence context, normalized using the observed trinucleotide frequency in the human exome to that in the human genome. The bars are ordered first by mutation class (C>A/G>T, C>G/G>C, C>T/G>A, T>A/A>T, T>C/A>G, T>G/A >C), then by the 5' flanking base (A, C, G, T) and then by the 3' flanking base (A, C, G, T). *: >20%.

Supplementary Fig. S1: Genome plots of copy number alterations. Genome plots of (A) tumors subjected to both whole-exome sequencing and OncoScan for copy number profiling and (B) the primary tumors and their synchronous distant metastases for cases 1-4, 6 and 8. In the genome plots, segmented Log2 ratios (y-axis) were plotted according to their genomic positions (x-axis). Alternating blue and grey demarcate the chromosomes. In (A), agreement of copy number status was assessed using Cohen's weighted kappa statistic.

Supplementary Table S1. Clinico-pathologic characteristics of primary breast cancers from our series and the METABRIC study and comparative analyses of the frequencies of somatic mutations found in the 341 genes analyzed. Supplementary Table S2. TP53 Sanger sequencing primers used in this study. Supplementary Table S3. Sequencing statistics Supplementary Table S4. Somatic mutations identified in 17 pairs of primary breast cancers and matched metastases using targeted massively parallel sequencing. Supplementary Table S5. Gene copy number alterations identified in the primary breast cancers and paired metastases. Supplementary Table S6. Pathway analyses of mutations and copy number aberrations that were enriched in metastases vs paired primary tumors per subtype Supplementary Table S7. Potentially targetable genetic alterations identified in primary breast cancers and matched metastases.

Supplementary Figure 1: Validation of somatic mutations in orthogonal methods. Supplementary Figure S2: Mutations and copy number alterations in ER-positive/HER2-negative male and female breast cancers. Supplementary Figure S3: Cancer cell fraction and clonality of non-synonymous mutations in male breast cancer.

Supplementary Fig. S2: Repertoire of somatic mutations found in the biopsies of primary breast cancers and their metastatic lesions in the nine treatment-naïve patients with de novo synchronous metastatic breast cancer. (A) Heatmaps indicate the cancer cell fraction of somatic mutations as determined by ABSOLUTE (24) (blue, see color key) or their absence (grey) in each biopsy (frozen and FFPE where available). Clonal mutations are highlighted in orange boxes and mutations associated with the loss of the wild-type allele are indicated with a diagonal bar. Red, dark grey and light grey dots beneath the heatmap indicate the mutation pathogenicity according to the color key. Mutations affecting cancer genes (31-33) are indicated by orange dots. Cases are grouped according to their ER and HER2 status. (B) Barplots of the distribution of mutations (top) present and (bottom) clonal in the frozen and FFPE diagnostic biopsies of the paired primary tumors and the metastatic lesions, classified as likely pathogenic (cancer genes), likely pathogenic (other genes), of indeterminate pathogenicity, likely passenger and synonymous mutations from all patients. Comparisons between the groups of mutations of different pathogenicity were performed using Fisher's exact tests. *: p < 0.05, **: p < 0.01, ***: p < 0.001, ns: not significant.

Supplementary Figure S6: Amplifications in ER-positive/HER2-negative male and female breast cancers. Supplementary Figure S7: Copy number alterations in ER-positive/HER2-negative male and non-lobular female breast cancers. Supplementary Figure S8: Copy number alterations in male and post-menopausal female breast cancers. Supplementary Figure S9: Copy number alterations in male and non-lobular female breast cancers. Supplementary Figure S10: Amplifications in male breast cancers and female breast cancers/ post-menopausal female breast cancers.

Supplementary Table S7: Comparative analysis of the frequencies of mutations in luminal A-like and luminal B-like male breast cancer. Supplementary Table S8: Comparative analysis of genes affecting selected functional pathways. Supplementary Table S9: Mutational frequency of male breast cancer vs subsets of female breast cancer from the TCGA study. Supplementary Table S10: Potentially targetable genes mutated exclusively in ER-positive/HER2-negative male breast cancer.

Supplementary Table S1: Histologic and immunohistochemical profiling and molecular classification of 59 male breast cancers. Supplementary Table S2: Antibody clones and amplification primers used. Supplementary Table S3: List of 241 genes included in the targeted capture massively parallel sequencing platform.

<p>A. Correlations between concordance percentage of mutations and time between primary tumor and paired metastases B. Influence of different adjuvant treatments on percentage of concordant, primary only or metastasis only mutations.</p>

Supplementary Table S2: Genomic analyses performed for the samples analyzed in the study and validation rates of mutations identified by wholeâ€exome sequencing and Validation of mutations identified by wholeâ€exome sequencing using targeted sequencing and amplicon sequencing.

Supplementary Table S1: Clinicopathologic characteristics of breast cancer patients with de novo metastastic disease included in the study.

Supplementary Table S11: Comparative analysis of the frequencies of mutations in luminal A-like male breast cancers and subsets of luminal A female breast cancers from the TCGA study. Supplementary Table S12: Comparative analysis of the frequencies of mutations in luminal B-like male breast cancers and subsets of luminal B female breast cancers from the TCGA study.

Supplementary Figure S4: Morphologic and immunophenotypic features of case MB-T88. Supplementary Figure S5: GATA3 mutational spectrum in male breast cancer and female breast cancer and the impact of selected mutations on the survival of male breast cancer patients.

You have accessJournal of UrologyCME1 May 2022PD59-01 BAP1 LOSS IS ASSOCIATED WITH METASTASIS IN ADVANCED CLEAR CELL RENAL CELL CARCINOMA Sari Khaleel, Anne Reiner, Cameron Brennan, Ibrahim Hussain, Samuel Berman, Ari Hakimi, and Nelson Moss Sari KhaleelSari Khaleel More articles by this author , Anne ReinerAnne Reiner More articles by this author , Cameron BrennanCameron Brennan More articles by this author , Ibrahim HussainIbrahim Hussain More articles by this author , Samuel BermanSamuel Berman More articles by this author , Ari HakimiAri Hakimi More articles by this author , and Nelson MossNelson Moss More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002644.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Mutations of the BAP1 tumor suppressor gene are found in 10-15% of clear cell renal cell carcinoma (ccRCC), portending a poor prognosis relative to BAP1-wildtype (BAP1-WT) tumors. However, it remains unclear if BAP1 loss predicts metastatic phenotype in ccRCC. We sought to assess the cumulative incidence of metastases in patients with BAP1-mutant (BAP1-MT) vs BAP1-WT ccRCC tumors. METHODS: We performed a retrospective review of 96 patients diagnosed with ccRCC with known BAP1 mutation status, managed at a tertiary care cancer center between 10/2001-9/2016, comparing patients with BAP1-MT (47 patients) to BAP1-WT (49 patients) tumors. For each patient, we retrieved clinical features and all metastatic events (MEs), accounting for the organ compartment and date of each individual ME noted on imaging and/or pathology reports. We hypothesized that BAP1-MT was associated with a higher likelihood of cumulative MEs overall, as well as a specific metastatic phenotype, defined as a predilection for cumulative MEs in one of two major compartments – bone or soft tissue (all non-bony compartments). Categorical and continuous outcomes were compared by BAP1 mutational status using Fisher’s exact test and Welch T-test, respectively, while cumulative incidence of MEs was compared between BAP1-MT and BAP1-WT using subdistribution hazard ratio (SD-HR) analysis. Analyses were performed using R package (R Foundation, v4.1). RESULTS: Median patient age at diagnosis was 58 years, with 20 (20.8%) patients presenting with metastatic disease at the time of diagnosis. Most patients were males (62, 64.6%), and underwent nephrectomy (84, 87.5%). Median follow-up was 52 months [IQR 22.25 – 78.25 months]. BAP1-MT and BAP1-WT did not differ significantly by age or BMI (p > 0.05). BAP1-MT patients had a higher AJCC stage on nephrectomy (p < 0.0001), with higher overall cumulative MEs (SD-HR 2.1, 95% CI 1.0-4.5, p = 0.05). Specifically, we noted a high incidence of MEs in bone (SD-HR 5.5, p = 0.02, 95% CI 1.3-23.5) followed by soft tissue (SD-HR 2.7, p = 0.001, 95% CI 1.5 – 5.0) (Figure 1). CONCLUSIONS: Mutations of the BAP1 gene were associated with higher stage disease and incidence of metastases overall, with a higher cumulative incidence of metastases in bone compared to soft tissue compartments, suggesting a potential metastatic phenotype for this mutation. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e1018 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Sari Khaleel More articles by this author Anne Reiner More articles by this author Cameron Brennan More articles by this author Ibrahim Hussain More articles by this author Samuel Berman More articles by this author Ari Hakimi More articles by this author Nelson Moss More articles by this author Expand All Advertisement PDF DownloadLoading ...

INTRODUCTION AND OBJECTIVE: Mutations of the BAP1 tumor suppressor gene are found in 10-15% of clear cell renal cell carcinoma (ccRCC), portending a poor prognosis relative to BAP1wildtype (BAP1-WT) tumors. However, it remains unclear if BAP1 loss predicts metastatic phenotype in ccRCC. We sought to assess the cumulative incidence of metastases in patients with BAP1-mutant (BAP1-MT) vs BAP1-WT ccRCC tumors. METHODS: We performed a retrospective review of 96 patients diagnosed with ccRCC with known BAP1 mutation status, managed at a tertiary care cancer center between 10/2001-9/2016, comparing patients with BAP1-MT (47 patients) to BAP1-WT (49 patients) tumors. For each patient, we retrieved clinical features and all metastatic events (MEs), accounting for the organ compartment and date of each individual ME noted on imaging and/or pathology reports. We hypothesized that BAP1-MT was associated with a higher likelihood of cumulative MEs overall, as well as a specific metastatic phenotype, defined as a predilection for cumulative MEs in one of two major compartments e bone or soft tissue (all non-bony compartments). Categorical and continuous outcomes were compared by BAP1 mutational status using Fisher's exact test and Welch T-test, respectively, while cumulative incidence of MEs was compared between BAP1-MT and BAP1-WT using subdistribution hazard ratio (SD-HR) analysis. Analyses were performed using R package (R Foundation, v4.1). RESULTS: Median patient age at diagnosis was 58 years, with 20 (20.8%) patients presenting with metastatic disease at the time of diagnosis. Most patients were males (62, 64.6%), and underwent nephrectomy (84, 87.5%). Median follow-up was 52 months [IQR 22.25 e 78.25 months]. BAP1-MT and BAP1-WT did not differ significantly by age or BMI (p > 0.05). BAP1-MT patients had a higher AJCC stage on nephrectomy (p < 0.0001), with higher overall cumulative MEs (SD-HR 2.1, 95% CI 1.0-4.5, p [ 0.05). Specifically, we noted a high incidence of MEs in bone (SD-HR 5.5, p [ 0.02, 95% CI 1.3-23.5) followed by soft tissue (SD-HR 2.7, p [ 0.001, 95% CI 1.5 e 5.0) (Figure 1). CONCLUSIONS: Mutations of the BAP1 gene were associated with higher stage disease and incidence of metastases overall, with a higher cumulative incidence of metastases in bone compared to soft tissue compartments, suggesting a potential metastatic phenotype for this mutation. Source of Funding: None

Abstract Genomic factors predictive of organ-specific tropism have been established in several models of cancer. However, the evolutionary dynamics at work in metastatic carcinoma have yet to be characterized in detail. We identified a cohort of clear cell renal cell carcinoma (RCC) patients who also had multiple metastasectomies, and performed deep sequencing and statistical inference of subclonal populations to infer phylogeny and essential genetic features acquired prior to systemic dissemination and site-specific colonization. Exome capture and deep sequencing were performed on tissues from 3 patients with polymetastatic RCC (including 12 metastases, multiple regions of primary tumors, and paired germline tissue) to a mean depth of 250x. Somatic point mutations were called with Mutect, and insertions and deletions with Strelka and VarScan. Validation was performed with a custom NimbleGen panel hybridized to a custom sequence library and sequenced to a mean depth of >500x. Allele-specific copy number and clonal prevalence were established using ABSOLUTE, and analyzed with Pyclone across primary and metastatic lesions to determine clonal architecture. Phylogenetic reconstruction identified ancestral clones, with attendant driver mutations in RCC tumor suppressors (including VHL, SETD2, PBRM1, MTOR) and independent subclonal populations in the metastases of all 3 patients. In an index case with multiple metastases separated spatially and temporally, bone and soft tissue metastases demonstrate apparent independent ancestors. Convergent loss of known tumor suppressors was also noted in all cases, and in several cases found in conjunction with de novo mutations in known RCC driver genes acquired late in tumor development. In this demonstration of subclonal and evolutionary analysis of multiple paired multi-organ RCC metastases, we identified subclonal populations characterized by alteration of several tumor suppressors which subsequently exhibited organ-specific patterns of metastasis.