Chimeras- Greek Mythology or Scientific Reality? Identification of Actionable Chimeric RNAs for Personalized Prevention of Breast Cancer

https://openworks.mdanderson.org/sumexp23/1080/thumbnail.jp

Association between Clinical Characteristics and Pathologic Nodal Stage in Hormone Receptor-Positive (HR+), Human Epidermal Growth Factor Receptor 2-Negative (HER2-) Breast Cancer

https://openworks.mdanderson.org/sumexp21/1228/thumbnail.jp

Utility of gross tumor palpability or tumor size as a measure of drug efficacy for cancer prevention

View full abstracthttps://openworks.mdanderson.org/leading-edge/1050/thumbnail.jp

Long-term avasimibe treatment abolishes the breast cancer preventative efficacy of statin in a spontaneous mouse model of breast cancer

View full abstracthttps://openworks.mdanderson.org/leading-edge/1023/thumbnail.jp

Radiomic and deep learning characterization of breast parenchyma on full field digital mammograms and specimen radiographs: A pilot study of a potential cancer field effect

Purpose: In women with biopsy-proven breast cancer, histologically normal areas of the parenchyma have shown molecular similarity to the tumor, supporting a potential cancer field effect. The purpose of this work was to investigate relationships of human-engineered radiomic and deep learning features between regions across the breast in mammographic parenchymal patterns and specimen radiographs. Approach: This study included mammograms from 74 patients with at least 1 identified malignant tumor, of whom 32 also possessed intraoperative radiographs of mastectomy specimens. Mammograms were acquired with a Hologic system and specimen radiographs were acquired with a Fujifilm imaging system. All images were retrospectively collected under an Institutional Review Board-approved protocol. Regions of interest (ROI) of 128 × 128 pixels were selected from three regions: within the identified tumor, near to the tumor, and far from the tumor. Radiographic texture analysis was used to extract 45 radiomic features and transfer learning was used to extract 20 deep learning features in each region. Kendall’s Tau-b and Pearson correlation tests were performed to assess relationships between features in each region. Results: Statistically significant correlations in select subgroups of features with tumor, near to the tumor, and far from the tumor ROI regions were identified in both mammograms and specimen radiographs. Intensity-based features were found to show significant correlations with ROI regions across both modalities. Conclusions: Results support our hypothesis of a potential cancer field effect, accessible radiographically, across tumor and non-tumor regions, thus indicating the potential for computerized analysis of mammographic parenchymal patterns to predict breast cancer risk.</p

Evaluation of sensitivity to endocrine herapy index (SET2,3) for response to neoadjuvant endocrine therapy and longer-term breast cancer patient outcomes (Alliance Z1031)

PURPOSE: To evaluate prediction of response and event-free survival (EFS) following neoadjuvant endocrine therapy by SET2,3 index of nonproliferation gene expression related to estrogen and progesterone receptors adjusted for baseline prognosis. EXPERIMENTAL DESIGN: A correlative study was conducted of SET2,3 measured from gene expression profiles of diagnostic tumor (Agilent microarrays) in 379 women with cStage II-III breast cancer from the American College of Surgeons Oncology Group Z1031 neoadjuvant aromatase inhibitor trial SET2,3 was dichotomized using the previously published cutoff. Fisher exact test was used to assess the association between SET2,3 and low proliferation at week 2-4 [Ki67 ≤ 10% or complete cell-cycle arrest (CCCA; Ki67 ≤ 2.7%)] and PEPI-0 rate in cohort B, and the association between SET2,3 and ypStage 0/I in all patients. Cox models were used to assess EFS with respect to SET2,3 excluding cohort B patients who switched to chemotherapy. RESULTS: Patients with high SET2,3 had higher rate of pharmacodynamic response than patients with low SET2,3 (Ki67 ≤ 10% in 88.2% vs. 56.9%, P \u3c 0.0001; CCCA in 50.0% vs. 26.2%, P = 0.0054), but rate of ypStage 0/I (24.0% vs. 20.4%, P = 0.4580) or PEPI = 0 (28.4% vs. 20.6%, P = 0.3419) was not different. Patients with high SET2,3 had longer EFS than patients with low SET2,3 (HR, 0.52, 95% confidence interval: 0.34-0.80; P = 0.0026). CONCLUSIONS: This exploratory analysis of Z1031 data demonstrated a higher rate of pharmacodynamic suppression of proliferation and longer EFS in high SET2,3 disease relative to low SET2,3 disease. The ypStage 0/I rate and PEPI = 0 rate were similar with respect to SET2,3

Atypical PKCiota Contributes to Poor Prognosis Through Loss of Apical-basal Polarity and Cyclin E Overexpression in Ovarian Cancer

We show that atypical PKCι, which plays a critical role in the establishment and maintenance of epithelial cell polarity, is genomically amplified and overexpressed in serous epithelial ovarian cancers. Furthermore, PKCι protein is markedly increased or mislocalized in all serous ovarian cancers. An increased PKCι DNA copy number is associated with decreased progression-free survival in serous epithelial ovarian cancers. In a Drosophila in vivo epithelial tissue model, overexpression of persistently active atypical PKC results in defects in apical-basal polarity, increased Cyclin E protein expression, and increased proliferation. Similar to the Drosophila model, increased PKCι proteins levels are associated with increased Cyclin E protein expression and proliferation in ovarian cancers. In nonserous ovarian cancers, increased PKCι protein levels, particularly in the presence of Cyclin E, are associated with markedly decreased overall survival. These results implicate PKCι as a potential oncogene in ovarian cancer regulating epithelial cell polarity and proliferation and suggest that PKCι is a novel target for therapy

Chimeric RNAs reveal putative neoantigen peptides for developing tumor vaccines for breast cancer

IntroductionWe present here a strategy to identify immunogenic neoantigen candidates from unique amino acid sequences at the junctions of fusion proteins which can serve as targets in the development of tumor vaccines for the treatment of breastcancer.MethodWe mined the sequence reads of breast tumor tissue that are usually discarded as discordant paired-end reads and discovered cancer specific fusion transcripts using tissue from cancer free controls as reference. Binding affinity predictions of novel peptide sequences crossing the fusion junction were analyzed by the MHC Class I binding predictor, MHCnuggets. CD8+ T cell responses against the 15 peptides were assessed through in vitro Enzyme Linked Immunospot (ELISpot).ResultsWe uncovered 20 novel fusion transcripts from 75 breast tumors of 3 subtypes: TNBC, HER2+, and HR+. Of these, the NSFP1-LRRC37A2 fusion transcript was selected for further study. The 3833 bp chimeric RNA predicted by the consensus fusion junction sequence is consistent with a read-through transcription of the 5’-gene NSFP1-Pseudo gene NSFP1 (NSFtruncation at exon 12/13) followed by trans-splicing to connect withLRRC37A2 located immediately 3’ through exon 1/2. A total of 15 different 8-mer neoantigen peptides discovered from the NSFP1 and LRRC37A2 truncations were predicted to bind to a total of 35 unique MHC class I alleles with a binding affinity of IC50&lt;500nM.); 1 of which elicited a robust immune response.ConclusionOur data provides a framework to identify immunogenic neoantigen candidates from fusion transcripts and suggests a potential vaccine strategy to target the immunogenic neopeptides in patients with tumors carrying the NSFP1-LRRC37A2 fusion