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PARTNER is a prospective, phase II-III, randomized controlled clinical trial that recruited patients with triple-negative breast cancer 1,2 , who were germline BRCA1 and BRCA2 wild type 3 . Here we report the results of the trial. Patients (n = 559) were randomized on a 1:1 basis to receive neoadjuvant carboplatin-paclitaxel with or without 150 mg olaparib twice daily, on days 3 to 14, of each of four cycles (gap schedule olaparib, research arm) followed by three cycles of anthracycline-based chemotherapy before surgery. The primary end point was pathologic complete response (pCR) 4 , and secondary end points included event-free survival (EFS) and overall survival (OS) 5 . pCR was achieved in 51% of patients in the research arm and 52% in the control arm (P = 0.753). Estimated EFS at 36 months in the research and control arms was 80% and 79% (log-rank P > 0.9), respectively; OS was 90% and 87.2% (log-rank P = 0.8), respectively. In patients with pCR, estimated EFS at 36 months was 90%, and in those with non-pCR it was 70% (log-rank P < 0.001), and OS was 96% and 83% (log-rank P < 0.001), respectively. Neoadjuvant olaparib did not improve pCR rates, EFS or OS when added to carboplatin-paclitaxel and anthracycline-based chemotherapy in patients with triple-negative breast cancer who were germline BRCA1 and BRCA2 wild type. ClinicalTrials.gov ID: NCT03150576 ., (© 2024. The Author(s).)

Competing Interests: I declare no competing interests.

Objectives: To investigate the relationship between magnetization transfer (MT) imaging and tissue macromolecules in high-grade serous ovarian cancer (HGSOC) and whether MT ratio (MTR) changes following neoadjuvant chemotherapy (NACT)., Methods: This was a prospective observational study. 12 HGSOC patients were imaged before treatment. MTR was compared to quantified tissue histology and immunohistochemistry. For a subset of patients ( n = 5), MT imaging was repeated after NACT. The Shapiro-Wilk test was used to assess for normality of data and Spearman's rank-order or Pearson's correlation tests were then used to compare MTR with tissue quantifications. The Wilcoxon signed-rank test was used to assess for changes in MTR after treatment., Results: Treatment-naïve tumour MTR was 21.9 ± 3.1% (mean ± S.D.). MTR had a positive correlation with cellularity, rho = 0.56 ( p < 0.05) and a negative correlation with tumour volume, ρ = -0.72 ( p = 0.01). MTR did not correlate with the extracellular proteins, collagen IV or laminin ( p = 0.40 and p = 0.90). For those patients imaged before and after NACT, an increase in MTR was observed in each case with mean MTR 20.6 ± 3.1% (median 21.1) pre-treatment and 25.6 ± 3.4% (median 26.5) post-treatment ( p = 0.06)., Conclusion: In treatment-naïve HGSOC, MTR is associated with cellularity, possibly reflecting intracellular macromolecular concentration. MT may also detect the HGSOC response to NACT, however larger studies are required to validate this finding., Advances in Knowledge: MTR in HGSOC is influenced by cellularity. This may be applied to assess for cell changes following treatment., Competing Interests: Competing interests: The authors have no conflicts of interest to declare., (© 2022 The Authors. Published by the British Institute of Radiology.)

Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment 1 . The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy 2 . Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery 3 were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers., (© 2021. The Author(s).)