Background State-of-the art therapy for recurrent ovarian cancer (ROC) suitable for platinum-based re-treatment includes bevacizumab-containing combinations (eg, carboplatin/paclitaxel, carboplatin/gemcitabine) or the most active non-bevacizumab regimen: carboplatin/pegylated liposomal doxorubicin (PLD). This head-to-head trial compared a standard bevacizumab-containing regimen versus carboplatin/PLD combined with bevacizumab. Methods In this multicentre, open-label, randomised, phase 3 trial, eligible patients had histologically confirmed epithelial ovarian, primary peritoneal, or fallopian tube carcinoma with first disease recurrence >6 months after first-line platinum-based chemotherapy, and were aged ≥18 years with Eastern Cooperative Oncology Group performance status 0-2. Patients were stratified by platinum-free interval, residual tumour, prior anti-angiogenic therapy, and study group language, and centrally randomised 1:1 using randomly permuted blocks of size two, four, or six to six intravenous cycles of carboplatin (AUC 4, day 1) plus gemcitabine (1000 mg/m 2 , days 1 and 8) every 3 weeks or six cycles of carboplatin (AUC 5, day 1) plus PLD (30 mg/m 2 , day 1) every 4 weeks, both given with bevacizumab (15 mg/kg every 3 weeks or 10 mg/kg every 2 weeks) until disease progression or toxicity. The primary endpoint was investigator-assessed progression-free survival (PFS). Efficacy data were analysed in the intention-to-treat population (all randomised patients). Safety was analysed in all patients who received at least one dose of study drug. This completed study is registered with ClinicalTrials.gov number NCT01837251.

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The preoperative serum level of creatinine may be useful as an additional independent prognostic parameter in patients with EOC.

Our data compare favorably to a theoretical cohort suggesting a clinically reasonable cut-off of > 11 mm endometrial thickness to discriminate between "normal" and "pathological". The data regarding "risk for endometrial cancer" can be used for counseling affected women.

Markedly elevated postoperative serum levels of CK and myoglobin levels might raise the suspicion for CS and could therefore aid in the rapid diagnosis of CS.

12065 Background: Long-term survivors (LTS) with ovarian cancer may be cured from cancer but frequently experience long-term toxicities such as fatigue with a huge impact on quality of life. Aim of this study was to evaluate factors associated with fatigue in LTS. Methods: Within the study “Carolin meets HANNA” ( www.carolinmeetshanna.com ) long-term survivors with ovarian cancer (LTS) were recruited since 11/2016. Long-term survival was defined as an ovarian cancer diagnosis more than eight years ago. Results: Until 12/2019 473 LTS could be recruited. 211 LTS (44.5%) have experienced fatigue. At the time point of recruitment in 23.4% (111 LTS) fatigue was still present. LTS with fatigue were not more frequently under current treatment compared to LTS without fatigue (p = 0.348). LTS with fatigue were not younger at initial diagnosis (50.4 vs. 51.9 years, p = 0.228). 58.6% of LTS with fatigue compared to 41.5% without fatigue have developed recurrent disease (p = 0.002) and LTS had more frequently more than one recurrence (66.1% vs. 51.7%, p = 0.055). Fatigue was associated with worse health status (2.9 vs. 2.2 on a scale from 1-5, p < 0.001). Fatigue was associated with medical complaints in general (82.0% vs. 43.0%, p < 0.001). Symptoms such as nausea and vomiting (p < 0.001), loss of appetite (p < 0.001), constipation (p < 0.001), diarrhea (p < 0.001), weight loss (p = 0.001) and bloating (p < 0.001) were more frequent in LTS with fatigue. This also accounts for cognitive disorders (39.6% vs. 10.5%, p < 0.001), depression (23.4% vs. 7.4%, p < 0.001), polyneuropathy (39.6% vs. 13.2%, p < 0.001) and cardiovascular disease (11.7% vs. 3.6%, p = 0.002). LTS with fatigue regard themselves more frequently as cancer patient (73.9% vs. 40.8%), p < 0.001). Conclusions: Fatigue is still very common in LTS despite the long survival time. Fatigue is associated with worsened health status and other long-term side effects underlining the impact on LTS. There is a high need for survivorship clinics that should ask for and, if necessary, should address still existing side effects such as fatigue.

Background: Adenosine signaling is a key metabolic pathway regulating tumor immunity. The conversion of inflammatory extracellular ATP into immunosuppressive adenosine invokes signaling of the A2a receptor (A2AR) in the tumor microenvironment. This dampens immune responses and creates a pro-tumor niche. A number of novel IO drugs targeting the adenosine pathway through inhibition of the ectonucleotidases CD39 and CD73 or the A2AR/BR are in clinical trials, including our non-CNS penetrant A2AR-selective antagonist, EXS21546 (NCT04727138). While early clinical results from other adenosine receptor-inhibitors (A2ARi) have shown modest monotherapy activity in nonspecific patient populations, we believe greater success can be achieved by leveraging methods that enable the evaluation of single-cell effects in patient samples preclinically. Using our deep learning driven image analysis platform, we define an adenosine-induced, tumor protective immunosuppression biomarker to augment A2AR antagonist responder identification. Here we describe efforts to transcriptionally and functionally map the adenosine suppressed immune potential and activation by inhibition of A2AR with EXS21546 in primary material. The goal is to reveal gene signatures indicative of adenosine immunosuppression deployable in clinical studies, increasing the likelihood of trial success by identifying patients that have the highest efficiency potential for A2AR targeted therapy. Methods: Leveraging patient material as disease relevant model systems, and collecting baseline and treatment condition transcriptomics, we model the patient specific anti-cancer immune repertoire and validate patient selection methods functionally with a translatable high content imaging platform amenable to primary human material supported by end-to-end deep learning driven image analysis. Results: Combining single cell transcriptomic and functional response data, we demonstrate preclinical mechanistic studies of A2AR antagonism on infiltrating immune cells, with the ultimate aim of discovering predictive algorithms to enrich patients more likely respond to adenosine pathway inhibitors. Patient selection gene signatures are functionally validated using a high content imaging platform with proven translational capabilities (Kornauth et al, Cancer Disc., 2021), demonstrating the association of anti-cancer immune activity with inhibition of adenosine signaling by EXS21546. Signatures and patient selection algorithms are cross-validated with publicly available data. Discussion: Gathering multiple layers of data from primary tumor tissues, we reveal and map the association of immune response potential to A2AR inhibition in cancer. Patient stratification gene signatures identified have the aim to be implemented in future studies of our candidate A2ARi EXS21546, to deliver the right drug at the right time in the right patients. Citation Format: Isabella Alt, Robert Shelke, Anna Lobley, Claudia Baumgaertler, Maja Stulic, Pierre Fons, Mark Whittaker, Klaus Hackner, Lucia Dzurillova, Edga Petru, Laudia Hadjari, Judith Lafleur, Josef Singer, Nikolaus Krall, Lukas Hefler, Thorsten Füreder, Christina Taubert, Christophe Boudesco, Andrew Payne, Gregory Ian Vladimer. Enriching for adenosine antagonist patient responses through deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4150.

Background: Precision cancer medicine aims to identify the right drug for the right patient, enriching for patients more likely to respond to a particular treatment. This paradigm is gaining importance during the early clinical lifecycle of a new potential drug to improve patient-centric trial designs, drive clinical success and eventually increase approval rates - enlarging the therapeutic arsenal available for oncology patients. To optimize the chance of success with our A2AR-selective antagonist, EXS-21546 (546; NCT04727138, discovered in collaboration with Evotec), we have identified an adenosine-induced immunosuppression biomarker signature (adenosine burden score or ABS) for clinical trial patient selection that also correlates with checkpoint inhibitor (CI) response prediction in ex vivo primary models. Here we present transcriptional and functional data mapping adenosine burden at the single cell level, and investigate subsequent modulation through antagonism of A2AR with 546, combination effects with CI, to prioritize patients for 546+CI therapy. Methods: By leveraging disease-relevant primary human tissues together with matched single cell and bulk transcriptomics, we assess adenosine-induced anticancer immune suppression and show initial biological confirmation of patient selection methodology and combination therapy effects with a translatable high content imaging platform (Kornauth et al 2021 & Snidjer et al 2017). Results: The ABS detects adenosine rich microenvironments with greater specificity and sensitivity than other published signatures. Validating the ABS in TGCA, we found the ABS anti-correlates with a validated predictor of anti-PD-1 therapy success (TIS, Damotte et al 2019), unraveling that high-adenosine/ABS cases are among patients least likely to respond to immunotherapy (low TIS). A2AR antagonism with 546 demonstrated a reduction of the adenosine burden, and restored the CI response potential as addressed by the ABS and TIS, respectively. Further immune reactivation was seen with antagonism of adenosine signaling by ‘546/CI combination ex vivo in primary tissues pre-selected with our ABS signature. Conclusions: Combining deep learning of single cell functional and multi-omics profiling data of disease relevant primary model systems, we model the association of the immune response potential to A2AR antagonism in cancer to define a biomarker signature to predict patients likely to benefit from A2AR antagonism and CI. This will be confirmed and validated retrospectively in an ongoing clinical study of 546 in two cancer indications. Citation Format: Isabella Alt, Robert Sehlke, Anna Lobley, Claudia Baumgaertler, Maja Stulic, Klaus Hackner, Lucia Dzurillova, Edgar Petru, Laudia Hadjari, Judith Lafleur, Josef Singer, Nikolaus Krall, Jozef Šufliarsky, Lukas Hefler, Thorsten Füreder, Christina Taubert, Andrew Payne, Christophe Boudesco, Gregory Ian Vladimer. Identification of transcript adenosine fingerprint to enrich for A2AR and PD-1 inhibition responders [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2151.