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Purpose: Deficiency in homologous recombination (HR) repair of DNA damage is characteristic of many high-grade serous ovarian cancers (HGSC). It is imperative to identify patients with homologous recombination–deficient (HRD) tumors as they are most likely to benefit from platinum-based chemotherapy and PARP inhibitors (PARPi). Existing methods measure historical, not necessarily current HRD and/or require high tumor cell content, which is not achievable for many patients. We set out to develop a clinically feasible assay for identifying functionally HRD tumors that can predict clinical outcomes. Experimental Design: We quantified RAD51, a key HR protein, in immunostained formalin-fixed, paraffin-embedded (FFPE) tumor samples obtained from chemotherapy-naïve and neoadjuvant chemotherapy (NACT)-treated HGSC patients. We defined cutoffs for functional HRD separately for these sample types, classified the patients accordingly as HRD or HR-proficient, and analyzed correlations with clinical outcomes. From the same specimens, genomics-based HRD estimates (HR gene mutations, genomic signatures, and genomic scars) were also determined, and compared with functional HR (fHR) status. Results: fHR status significantly predicted several clinical outcomes, including progression-free survival (PFS) and overall survival (OS), when determined from chemo-naïve (PFS, P < 0.0001; OS, P < 0.0001) as well as NACT-treated (PFS, P < 0.0001; OS, P = 0.0033) tumor specimens. The fHR test also identified as HRD those PARPi-at-recurrence–treated patients with longer OS (P = 0.0188). Conclusions: We developed an fHR assay performed on routine FFPE specimens, obtained from either chemo-naïve or NACT-treated HGSC patients, that can significantly predict real-world platinum-based chemotherapy and PARPi response.

Introduction: Currently, there is a trend towards non-invasive hemodynamic monitoring guided by echocardiography, however, in our center, in patients undergoing coronary revascularization surgery, invasive hemodynamic monitoring with Swan-Ganz catheter placement continues. Given that hemodynamic monitoring using non-invasive methods has been shown to be feasible, it is therefore expected that the hemodynamic variables obtained by echocardiography are consistent with the variables obtained by Swan-Ganz catheterization in this type of patient. The objective of the study is to evaluate the concordance that exists between the different hemodynamic variables obtained by Swan-Ganz catheterization, with the variables obtained by echocardiography. Methods: Observational, non-interventional, retrospective, cross-sectional and analytical study, non-randomized. Patients admitted to the Coronary Unit after coronary revascularization surgery were selected, who had a Swan-Ganz catheter on admission, all patients underwent measurement of the hemodynamic variables CO, RAP, PASP, PWP and later TTE was performed to obtain the same variables. Finally, the Concordance Correlation Coefficient described by Lin CCC= (A2+B2-C2) ÷(A2+B2+D2) and by the Bland-Altman method was used as a statistical tool. Results: 26 patients were included, with a mean age of 63.8 ± 6.2 years, 69.2% male. For the CO variable, an average absolute difference of 0.77 L/min and a CCC of 0.7334 were obtained; in the analysis of (B-A), an average difference is -0.4149 L/min. For the RAP an average difference of 2.11 mmHg, with a CCC = 0.8595 and in the analysis of B-A an average difference of -1.11 mmHg. For the PASP an average absolute difference of 5.11 mmHg was obtained with a CCC = 0.5444 and in the B-A analysis an average difference of 0.2944 mmHg. For PWP an average absolute difference of 2.78 mmHg with a CCC = 0.3842, in the analysis of B-A an average difference of 2.13 mmHg. When carrying out the analysis of the average differences obtained from each of the variables, with which a P value of 0.293 was obtained for the CO, 0.129 for the RAP, 0.308 for the PASP, and 0.017 for the PWP. Conclusions: A poor strength of agreement was observed for the CCC for each of the variables, however, these are not clinically significant, so they can be considered as interchangeable methods. It is important to mention that the TTE provides other relevant parameters in the management of these patients.

Comparison of different fHR score cut-off values.

Tumor percentages estimated from H&E images and comparison between HR, ovaHRDScar and Telli2016 status.

Purpose:Deficiency in homologous recombination (HR) repair of DNA damage is characteristic of many high-grade serous ovarian cancers (HGSC). It is imperative to identify patients with homologous recombination–deficient (HRD) tumors as they are most likely to benefit from platinum-based chemotherapy and PARP inhibitors (PARPi). Existing methods measure historical, not necessarily current HRD and/or require high tumor cell content, which is not achievable for many patients. We set out to develop a clinically feasible assay for identifying functionally HRD tumors that can predict clinical outcomes.Experimental Design:We quantified RAD51, a key HR protein, in immunostained formalin-fixed, paraffin-embedded (FFPE) tumor samples obtained from chemotherapy-naïve and neoadjuvant chemotherapy (NACT)-treated HGSC patients. We defined cutoffs for functional HRD separately for these sample types, classified the patients accordingly as HRD or HR-proficient, and analyzed correlations with clinical outcomes. From the same specimens, genomics-based HRD estimates (HR gene mutations, genomic signatures, and genomic scars) were also determined, and compared with functional HR (fHR) status.Results:fHR status significantly predicted several clinical outcomes, including progression-free survival (PFS) and overall survival (OS), when determined from chemo-naïve (PFS, P < 0.0001; OS, P < 0.0001) as well as NACT-treated (PFS, P < 0.0001; OS, P = 0.0033) tumor specimens. The fHR test also identified as HRD those PARPi-at-recurrence–treated patients with longer OS (P = 0.0188).Conclusions:We developed an fHR assay performed on routine FFPE specimens, obtained from either chemo-naïve or NACT-treated HGSC patients, that can significantly predict real-world platinum-based chemotherapy and PARPi response.

Exploring non-genetic evolution of cell states during cancer treatments has become attainable by recent advances in lineage-tracing methods coupling cell states to future fates. However, transcriptional changes that drive pre-treatment cells into resistant fates may be subtle, necessitating high resolution analysis. We developed ReSisTrace that uses shared transcriptomic features of synchronised sister cells to predict the states that prime treatment resistance, and allows identification of asymmetric features that drive phenotypic heterogeneity. Applying ReSisTrace in ovarian cancer cells revealed that BRCAness transcriptional signatures are associated with pre-existing vulnerability not only to olaparib and carboplatin treatments, but also to natural killer cell-mediated cytotoxicity. This novel connection between DNA repair defect and susceptibility to natural killer cells was further validated both functionally and in a clinical cohort. The high-resolution analysis by ReSisTrace enables resolving pre-existing transcriptional features of treatment vulnerability, facilitating molecular patient stratification for personalised therapies.

Homologous recombination DNA-repair deficiency (HRD) is a common driver of genomic instability and confers a therapeutic vulnerability in cancer. The accurate detection of somatic allelic imbalances (AIs) has been limited by methods focused onBRCA1/2mutations and using mixtures of cancer types. Using pan-cancer data, we revealed distinct patterns of AIs in high-grade serous ovarian cancer (HGSC). We used machine learning and statistics to generate improved criteria to identify HRD in HGSC (ovaHRDscar). ovaHRDscar significantly predicted clinical outcomes in three independent patient cohorts with higher precision than previous methods. Characterization of 98 spatiotemporally distinct metastatic samples revealed low intra-patient variation and indicated the primary tumor as the preferred site for clinical sampling in HGSC. Further, our approach improved the prediction of clinical outcomes in triple-negative breast cancer (tnbcHRDscar), validated in two independent patient cohorts. In conclusion, our tumor-specific, systematic approach has the potential to improve patient selection for HR-targeted therapies.