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1. Breast cancer models: Engineering the tumor microenvironment

   https://doi.org/10.1016/j.actbio.2020.02.006  

   Bahcecioglu, Gokhan; Basara, Gozde; Ellis, Bradley W; Ren, Xiang; Zorlutuna, Pinar (April 2020, Acta Biomaterialia)
2. PDJ amplicon in triple negative breast cancer

   https://doi.org/10.1038/s41598-023-27887-8  

   Roesler, Alexander S.; Malasi, Smriti; Koslosky, Lori; Hartmayer, Peter; Naab, Tammey J.; Carter, Jodi M.; Zahrieh, David; Hillman, David; Leon-Ferre, Roberto A.; Couch, Fergus J.; et al (December 2023, Scientific Reports)

   Abstract Amplification of chromosome 9p24.1 targeting PD-L1 , PD-L2 , and JAK2 (PDJ amplicon) is present in subsets of triple negative breast cancers (TNBCs) and is associated with poor clinical outcomes. However, the prevalence of PDJ+ TNBCs varies extensively across studies applying different methods for interrogating samples of interest. To rigorously assess the prevalence of PDJ amplicons in TNBC, its prognostic value and whether it is enriched by chemotherapy, we interrogated 360 TNBC samples including 74 surgical resections from patients treated in the neoadjuvant setting, and tissue microarrays (TMAs) with 31 cases from African American women and 255 resected non-metastatic cases, with a 3 color fluorescence in situ hybridization (FISH) assay targeting the 9p24.1 PDJ amplicon, 9q24.3, and 9q34.1. Samples with mean PDJ signal of > 4.5 copies, and ratios of PDJ/9q24 ≥ 2 and/or PDJ/9q34.1 ≥ 2 were called amplified (PDJ+). Correlative analyses included the association of tumor infiltrating lymphocytes (TILs) with PDJ amplicons in TNBCs. In addition, we investigated intratumor copy number of PDJ amplicons in PDJ+ and PDJ− TNBCs. Matched pre- and post-neoadjuvant treatment biopsies were available from patients (n = 6) to evaluate the effects of therapy on PDJ status. Our study provides a rigorous analysis of the prevalence, distribution, and clinical correlatives of the PDJ amplicon in TNBC. 

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3. Pseudotime Based Discovery of Breast Cancer Heterogeneity

   https://doi.org/10.1109/BIBM47256.2019.8983300  

   Aqila, Tasmia; Mamun, Abdullah Al; Mondal, Ananda Mohan (November 2019, 2019 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM))

   Breast cancer is highly sporadic and heterogeneous in nature. Even the patients with same clinical stage do not cluster together in terms of genomic profiles such as mRNA expression. In order to prevent and cure breast cancer completely, it is essential to decipher the detailed heterogeneity of breast cancer at genomic level. Putting the cancer patients on a time scale, which represents the trajectory of cancer development, may help discover the detailed heterogeneity. This in turn would help establish the mechanisms for prevention and complete cure of breast cancer. The goal of this study is to discover the heterogeneity of breast cancer by ordering the cancer patients using pseudotime. This is achieved through two objectives: First, a computational framework is developed to place the cancer patients on a time scale, meaning construct a trajectory of cancer development, by inferring pseudotime from static mRNA expression data; Second, discovering breast cancer heterogeneity at different time periods of the trajectory using statistical and machine learning techniques. In this study, the trajectory of breast cancer progression was constructed using static mRNA expression profiles of 1072 breast cancer patients by inferring pseudotime. Three sets of key genes discovered using supervised machine learning techniques are used to develop the trajectories. The first set of genes are PAM50 genes which is available in literature. The second and third sets of genes were discovered in the present study using the clinical stages of breast cancer (Stage-I, Stage-II, Stage-III, and Stage-IV). The proposed computational framework has the capability of deciphering heterogeneity in breast cancer at a granular level. The results also show the existence of multiple parallel trajectories at different time periods of cancer development or progression. 

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4. Deep Generative Breast Cancer Screening and Diagnosis

   https://doi.org/10.1007/978-3-030-00934-2_95  

   Shams, Shayan; Platania, Richard; Zhang, Jian; Kim, Joohyun; Lee, Kisung; Park, Seung-Jong (September 2018, International Conference on Medical Image Computing and Computer-Assisted Intervention)
5. Biomechanical Microenvironment Regulates Fusogenicity of Breast Cancer Cells

   https://doi.org/10.1021/acsbiomaterials.8b00861  

   Zhu, Peiran; Tseng, Ning-Hsuan; Xie, Tianfa; Li, Ningwei; Fitts-Sprague, Isaac; Peyton, Shelly R.; Sun, Yubing (January 2019, ACS Biomaterials Science & Engineering)