TCGA, African-American (n= 129,%) Unveiling the African Breast Cancer Genome: The West Africa Breast Cancer Study (WABCS) Emma Labrot1, Abayomi Odetunde2, Markus Riester1, Toshio Yoshimatsu3, Adeyinka Ademola2, Ayodele Sanni4, Babajide Okedere2, Scott Mahan1, Ifeanyi Nwosu2, Artur Veloso1, Rebecca Leary1, Mustapha Ajani2, Ryan S. Johnson1, Elisabeth Sveen3, Yonglan Zheng3, Wendy Clayton3, Galina Khramtsova3, Mobolaji Oludara4, Foluso Omodele4, Odunayo Benson2, Kenzie MacIsaac1, Adewumi Adeoye2, Oludare Morhason-Bello2, Temidayo Ogundiran2, Chinedum Babalola2, Abiodun Popoola4, Michael Morrissey1, Dezheng Huo3, Adeyinka Falusi2, Wendy Winckler1, John Obafunwa4, Dimitris Papoutsakis1, Oladosu Ojengbede2, Nasiru Ibrahim4, Olayiwola Oluwasola2, Olufunmilayo I. Olopade3, Jordi Barretina1 1. Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, USA; 2. Institute for Advanced Medical Research and Training, The University of Ibadan College of Medicine, Ibadan, Nigeria; 3. The University of Chicago, Chicago, IL, USA; 4. Lagos State University Teaching Hospital, Ikeja, Lagos State, Nigeria Introduction Aggressive Subtypes are Overrepresented in WABCS Significantly Mutated Genes in the WABCS Data Set Across races, breast cancer incidence and mortality rates markedly differ. Numerous studies have demonstrated that individuals with African ancestry acquire aggressive, early-onset breast cancers more frequently than other populations. The sources of these disparities are not known, and a comprehensive characterization of mutation landscapes amongst African, African Americans, and Caucasian breast tumors has not been performed. We have developed a cross-continent research infrastructure to examine the spectrum of genomic alterations in breast tumors from West Africa and subsequently compare them to Caucasian and African American breast tumors from The Cancer Genome Atlas (TCGA). Gene SNVs Total Muts Muts/Mbp p-value FDR # Validated by RNAseq TP53 58 81 492.48 0.000 18 of 20 PIK3CA 25 26 67.85 5 of 9 CAND1 7 14.79 0.016 1 of 1 PARD6B 4 27.96 0.008 3 of 3 FOXA1 21.82 0.053 1 of 2 MED25 13.98 0.065 SEC14L1 14.3 TTLL3 11.35 0.074 GBA2 2 11.07 2 of 2 BAK1 3 38.14 0.004 HNRNPD 22.92 0.062 HNRNPL 13.6 TMTC4 10.18 EIF2B4 13.86 0.001 0.091 AKT1 16 0.038 XPO6 6.94 0.088 1 of 3 PDE6B 8.99 0.084 XBP1 1 20.04 0.066 LYRM7 57.87 0.034 ROGDI 18.18 0.030 CDK2AP2 40.27 0.089 GRB7 9.22 0.049 VPS28 34.13 0.100 ITPK1 12.63 0.056 TRMT5 10.19 0.094 PAM50 subtyping across WABCS samples. 34% were classified as basal and 26% were classified as Her2 amplified. When compared to TCGA, only 14% of non-African American/black breast cancer samples were classified as basal and 4% as Her2 amplified, this data supports previous claims that more aggressive subtypes are over represented in breast cancers from patients of African decent. WABCS and TCGA Breast Cancer Patient Characteristics Frequency of Genetic Alterations in WABCS Characteristic WABCS (n=129; %) TCGA, African-American (n= 129,%) TCGA, others (n=974, %) Age at Diagnosis, years   Median 48.5 54 58 IQR 41-57 47-66 48-66 Subtype Composition Triple Negative 37 28 11 HER2 Positive 45 18 25 Hormone Receptor (ER/PR) Positive 64 MuSiC analysis of significantly mutated genes in the WABCS data set. Mutations with supporting RNAseq data in at least 50% of samples was used as an additional parameter to a p-value cut off of 0.01 and an FDR < 0.10. Differences in Immune Microenvironment Revealed by RNAseq WABCS Workflow “Oncoprint” displaying the genetic alterations in genes determined by TCGA to be involved in breast cancer. For IHC data (top panels): black boxes = negative, red boxes = positive, gray boxes = data unavailable. Age/Ploidy heat maps (middle panels): Scale from pale yellow to red; red indicates older patients/tumors with higher ploidy and light yellow indicates younger patients/diploid tumors; white = no data Researchers were trained both on site in Nigeria and at Novartis on DNA/RNA extraction, tissue sectioning and immunohistochemistry (IHC). Sequencing data analysis: Single nucleotide variants and insertions/deletions were called using MuTect and PINDEL; copy number alterations were called using an in-house implementation of ABSOLUTE. Comparison of Genetic Alteration Frequencies in Triple Negative Breast Cancers WABCS Dataset Expression of immune cell signatures across breast cancer data sets. AA = African American/Black TCGA breast cancer samples Signatures were derived by averaging the expression of genes that are considered hallmark of that particular immune cell type. For example, CSF1R, CD68, ITGAX, and ITGAM gene expression are incorporated to the “Macrophages” signature 435 tumor biopsies collected at the oncology clinics in Nigeria Samples were analyzed by IHC for tumor content, ER, PR and HER2. Samples with greater than 30% tumor were processed for DNA and RNA extraction. 195 tumor/normal pairs sent to Novartis’ Next Generation Diagnostics (NGDx) group. Samples that yielded at least 500µg of DNA were further processed for sequencing. 129 tumor/normal DNA pairs have been successfully sequenced and analyzed to date. 126 RNA samples extracted in Nigeria and sent to NGDx. Samples with at least 350ng of RNA and a RIN score of 4 were further processed for sequencing. 65 RNA samples have successfully undergone RNAseq and have been analyzed to date. CCND1* AKT1* GATA3* Conclusions The West Africa Breast Cancer Study is the largest genomic data set of African breast cancer patient samples to date. This collaboration has been able to provide and support the infrastructure of two Nigerian research laboratories that are now able to successfully extract DNA and RNA suitable for sequencing studies. Genomic profiling revealed both known and novel alterations in African breast cancer samples. RNAseq-based immune profiling may uncover unique immune features in these patient populations that could inform the potential use of immune therapies. * = FDR <0.05