bims-ovdlit 2021-08-01 papers

Gynecol Oncol. 2021 Aug;pii: S0090-8258(21)00437-6. [Epub ahead of print]162(2): 375-381

Progression-free survival by investigator versus blinded independent central review in newly diagnosed patients with high-grade serous ovarian cancer: Analysis of the VELIA/GOG-3005 trial.

Carol Aghajanian, Michael A Bookman, Gini F Fleming, Elizabeth M Swisher, Karina D Steffensen, Michael Friedlander, Aikou Okamoto, Camille Gunderson Jackson, Danielle Sullivan, Christine K Ratajczak, Robert L Coleman.

OBJECTIVE: In the phase 3 VELIA/GOG-3005 trial, veliparib added to carboplatin-paclitaxel and continued as maintenance improved progression-free survival (PFS) compared to carboplatin-paclitaxel alone in patients with newly diagnosed ovarian carcinoma. Primary analysis of PFS was by investigator (INV) assessment, with a supplemental analysis of PFS by blinded independent central review (BICR).METHODS: Patients received veliparib or placebo with carboplatin-paclitaxel (6 cycles) and as maintenance (30 additional cycles). The primary analysis compared PFS in the veliparib-throughout arm to the carboplatin-paclitaxel only arm in the BRCA mutation (BRCAm), homologous recombination deficiency (HRD), and intention-to-treat (ITT) populations. Exploratory analyses of PFS in BRCA wildtype (BRCAwt), homologous recombination proficient (HRP), and HRD + BRCAwt populations were also performed. PFS per BICR and overall concordance rates between INV and BICR assessments were analyzed.
RESULTS: Hazard ratios for PFS by INV and BICR were consistent in each of the primary analysis and exploratory populations. In the ITT population, median PFS per INV was 23.5 months in the veliparib-throughout arm versus 17.3 months in the control arm (hazard ratio [HR] 0.683, 95% confidence interval [CI] 0.562-0.831; P < 0.001). Median PFS by BICR was 29.3 months versus 19.2 months (HR 0.687, 95% CI 0.504-0.806). In the ITT population, the overall concordance rates between INV and BICR were 78% and 75% for the veliparib-throughout and control arms, respectively.
CONCLUSIONS: Hazard ratios for PFS per BICR and per INV were consistent, with no suggestion of investigator bias. These findings support the reliability of PFS by INV in ovarian cancer trials.

Keywords: BICR; GOG-3005; Ovarian cancer; PARP inhibitor; Phase 3; VELIA; Veliparib

DOI: https://doi.org/10.1016/j.ygyno.2021.05.031

Cancer Res. 2021 Jul 28. pii: canres.0774.2021. [Epub ahead of print]

Acquired RAD51C promoter methylation loss causes PARP inhibitor resistance in high grade serous ovarian carcinoma.

In high-grade serous ovarian carcinoma (HGSC), deleterious mutations in DNA repair gene RAD51C are established drivers of defective homologous recombination and are emerging biomarkers of PARP inhibitor (PARPi) sensitivity. RAD51C promoter methylation (meRAD51C) is detected at similar frequencies to mutations, yet its effects on PARPi responses remain unresolved. In this study, three HGSC patient-derived xenograft (PDX) models with methylation at most or all examined CpG sites in the RAD51C promoter show responses to PARPi. Both complete and heterogeneous methylation patterns were associated with RAD51C gene silencing and homologous recombination deficiency (HRD). PDX models lost meRAD51C following treatment with PARPi rucaparib or niraparib, where a single unmethylated copy of RAD51C was sufficient to drive PARPi resistance. Genomic copy number profiling of one of the PDX models using SNP arrays revealed that this resistance was acquired independently in two genetically distinct lineages. In a cohort of 11 patients with RAD51C-methylated HGSC, various patterns of meRAD51C were associated with genomic 'scarring', indicative of HRD history, but exhibited no clear correlations with clinical outcome. Differences in methylation stability under treatment pressure were also observed between patients, where one HGSC was found to maintain meRAD51C after 6 lines of therapy (4 platinum-based), whilst another HGSC sample was found to have heterozygous meRAD51C and elevated RAD51C gene expression (relative to homozygous meRAD51C controls) after only neo-adjuvant chemotherapy. As meRAD51C loss in a single gene copy was sufficient to cause PARPi resistance in PDX, methylation zygosity should be carefully assessed in previously treated patients when considering PARPi therapy.

DOI: https://doi.org/10.1158/0008-5472.CAN-21-0774

NAR Cancer. 2021 Sep;3(3): zcab028

Characterization of a RAD51C-silenced high-grade serous ovarian cancer model during development of PARP inhibitor resistance.

Acquired PARP inhibitor (PARPi) resistance in BRCA1- or BRCA2-mutant ovarian cancer often results from secondary mutations that restore expression of functional protein. RAD51C is a less commonly studied ovarian cancer susceptibility gene whose promoter is sometimes methylated, leading to homologous recombination (HR) deficiency and PARPi sensitivity. For this study, the PARPi-sensitive patient-derived ovarian cancer xenograft PH039, which lacks HR gene mutations but harbors RAD51C promoter methylation, was selected for PARPi resistance by cyclical niraparib treatment in vivo. PH039 acquired PARPi resistance by the third treatment cycle and grew through subsequent treatment with either niraparib or rucaparib. Transcriptional profiling throughout the course of resistance development showed widespread pathway level changes along with a marked increase in RAD51C mRNA, which reflected loss of RAD51C promoter methylation. Analysis of ovarian cancer samples from the ARIEL2 Part 1 clinical trial of rucaparib monotherapy likewise indicated an association between loss of RAD51C methylation prior to on-study biopsy and limited response. Interestingly, the PARPi resistant PH039 model remained platinum sensitive. Collectively, these results not only indicate that PARPi treatment pressure can reverse RAD51C methylation and restore RAD51C expression, but also provide a model for studying the clinical observation that PARPi and platinum sensitivity are sometimes dissociated.

DOI: https://doi.org/10.1093/narcan/zcab028

Nanomedicine. 2021 Jul 23. pii: S1549-9634(21)00095-2. [Epub ahead of print] 102452

CELL-STIFFNESS AND MORPHOLOGICAL ARCHITECTURAL PATTERNS IN CLINICAL SAMPLES OF HIGH GRADE SEROUS OVARIAN CANCERS.

E Azzalini, N Abdurakhmanova, P Parisse, M Bartoletti, V Canzonieri, G Stanta, L Casalis, S Bonin.

High grade serous ovarian carcinoma (HGSOC) is recognized as the most frequent type of ovarian cancer and the main cause of ovarian cancer related deaths worldwide. Although homologous recombination deficiency testing has been adopted in the clinical workflow, morphological analysis remains the main diagnostic tool. In this study Atomic Force Microscopy (AFM) was tested in standard hematoxylin and eosin (H&E) stained sections to investigate the biomechanical properties of different architectural growing patterns of HGSOC. Our results showed that AFM was able to discriminate HGSOC morphological growing patterns as well as patients' stage. Micropapillary pattern, which has been associated to poor outcome had lower Young's moduli. In addition stage IV HGSOC was significantly softer than stage III cancers. Taken our results, AFM analysis could represent an additional tool in HGSOC morphological diagnosis as the biomechanical proprieties of HGSOC were quantitatively associated to tumor staging and architectural pattern.

Keywords: AFM; FFPE tissues; HGSOC; Young’s modulus; cancer morphological architectures

DOI: https://doi.org/10.1016/j.nano.2021.102452

Cancer Cell. 2021 Jul 16. pii: S1535-6108(21)00381-0. [Epub ahead of print]

From bench to bedside: single-cell analysis for cancer immunotherapy.

Emily F Davis-Marcisak, Atul Deshpande, Genevieve L Stein-O'Brien, Won J Ho, Daniel Laheru, Elizabeth M Jaffee, Elana J Fertig, Luciane T Kagohara.

Single-cell technologies are emerging as powerful tools for cancer research. These technologies characterize the molecular state of each cell within a tumor, enabling new exploration of tumor heterogeneity, microenvironment cell-type composition, and cell state transitions that affect therapeutic response, particularly in the context of immunotherapy. Analyzing clinical samples has great promise for precision medicine but is technically challenging. Successfully identifying predictors of response requires well-coordinated, multi-disciplinary teams to ensure adequate sample processing for high-quality data generation and computational analysis for data interpretation. Here, we review current approaches to sample processing and computational analysis regarding their application to translational cancer immunotherapy research.

Keywords: computational biology; single-cell proteomics; single-cell transcriptomics; spatial proteomics; spatial transcriptomics; translational medicine; tumor immunology

DOI: https://doi.org/10.1016/j.ccell.2021.07.004