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Objectives In the Cancer Core Europe Consortium (CCE), standardized biomarkers are required for therapy monitoring oncologic multicenter clinical trials. Multiparametric functional MRI and particularly diffusion-weighted MRI offer evident advantages for noninvasive characterization of tumor viability compared to CT and RECIST. A quantification of the inter- and intraindividual variation occurring in this setting using different hardware is missing. In this study, the MRI protocol including DWI was standardized and the residual variability of measurement parameters quantified. Methods Phantom and volunteer measurements (single-shot T2w and DW-EPI) were performed at the seven CCE sites using the MR hardware produced by three different vendors. Repeated measurements were performed at the sites and across the sites including a traveling volunteer, comparing qualitative and quantitative ROI-based results including an explorative radiomics analysis. Results For DWI/ADC phantom measurements using a central post-processing algorithm, the maximum deviation could be decreased to 2%. However, there is no significant difference compared to a decentralized ADC value calculation at the respective MRI devices. In volunteers, the measurement variation in 2 repeated scans did not exceed 11% for ADC and is below 20% for single-shot T2w in systematic liver ROIs. The measurement variation between sites amounted to 20% for ADC and < 25% for single-shot T2w. Explorative radiomics classification experiments yield better results for ADC than for single-shot T2w. Conclusion Harmonization of MR acquisition and post-processing parameters results in acceptable standard deviations for MR/DW imaging. MRI could be the tool in oncologic multicenter trials to overcome the limitations of RECIST-based response evaluation. Key Points • Harmonizing acquisition parameters and post-processing homogenization, standardized protocols result in acceptable standard deviations for multicenter MR–DWI studies. • Total measurement variation does not to exceed 11% for ADC in repeated measurements in repeated MR acquisitions, and below 20% for an identical volunteer travelling between sites. • Radiomic classification experiments were able to identify stable features allowing for reliable discrimination of different physiological tissue samples, even when using heterogeneous imaging data.

Plots showing percentage changes in total cfDNA concentration for each patient at weeks 4 and 8 of treatment, compared to same-patient baseline value. The left plot includes non-responding patients and the right plot includes patients who responded to olaparib in the clinical trial. Below, Mann-Whitney test comparing these changes at each time point for responders and non-responder patients.

Supplementary figure and table legends

Supplementary Data 6: Monitoring of somatic genomic alterations in plasma during targeted therapy. Allele frequencies (AF) of identified mutations are represented on the left Y-axis while the sum of the target lesions on the CT scan, are represented on the right Y-axis.

Additional Information on molecular analyses determining CHD1 and SPOP status

Supplementary Data 1: Primers and probes for ddPCR assay Supplementary Data 2: Consort Diagram Supplementary Data 3: List of drugs Supplementary Data 4: Cross validation of PGM sequencing assay with ddPCR Supplementary Data 5: To test intra-run assay performance, 3 different libraries were prepared using the same plasma cfDNA from a single patient Supplementary Data 6: See figure 6 below. Supplementary Data 7: Time to progression for patients having a 30% decrease in plasma mutation AF, following targeted drug administration, and association with time to progression by RECIST evaluation.

Sample overview

Figure S1. Flowchart of study selection process with ICIs treatment. Figure S2. Flowchart of study selection process with TAs treatment. Figure S3. Overall Survival comparing HPD with non-HPD in patients with progression disease as best response in the ICIs and TAs cohorts ( RECIST 1.1 progression disease Landmark Analysis). Figure S4. Overall Survival in second line (a) and third (b) setting HPD vs non-HPD progressors by RECIST criteria in ICIs cohort. Figure S5. Concordance between RECIST and TGR criteria in ICIs cohort. Table S1. Computed tomography (CT) acquisition protocol parameters. Table S2. Clinical variables in patients treated with TAs. Table S3. Multivariate model in patients with progression disease as best response in ICIs group (using TGR).

SPOP mutation identified in this cohort.

Purpose: CHD1 deletions and SPOP mutations frequently cooccur in prostate cancer with lower frequencies reported in castration-resistant prostate cancer (CRPC). We monitored CHD1 expression during disease progression and assessed the molecular and clinical characteristics of CHD1-deleted/SPOP-mutated metastatic CRPC (mCRPC).Experimental Design: We identified 89 patients with mCRPC who had hormone-naive and castration-resistant tumor samples available: These were analyzed for CHD1, PTEN, and ERG expression by IHC. SPOP status was determined by targeted next-generation sequencing (NGS). We studied the correlations between these biomarkers and (i) overall survival from diagnosis; (ii) overall survival from CRPC; (iii) duration of abiraterone treatment; and (iv) response to abiraterone. Relationship with outcome was analyzed using Cox regression and log-rank analyses.Results: CHD1 protein loss was detected in 11 (15%) and 13 (17%) of hormone-sensitive prostate cancer (HSPC) and CRPC biopsies, respectively. Comparison of CHD1 expression was feasible in 56 matched, same patient HSPC and CRPC biopsies. CHD1 protein status in HSPC and CRPC correlated in 55 of 56 cases (98%). We identified 22 patients with somatic SPOP mutations, with six of these mutations not reported previously in prostate cancer. SPOP mutations and/or CHD1 loss was associated with a higher response rate to abiraterone (SPOP: OR, 14.50 P = 0.001; CHD1: OR, 7.30, P = 0.08) and a longer time on abiraterone (SPOP: HR, 0.37, P = 0.002, CHD1: HR, 0.50, P = 0.06).Conclusions: SPOP-mutated mCRPCs are strongly enriched for CHD1 loss. These tumors appear highly sensitive to abiraterone treatment. Clin Cancer Res; 24(22); 5585–93. ©2018 AACR.

Purpose:Most hyperprogression disease (HPD) definitions are based on tumor growth rate (TGR). However, there is still no consensus on how to evaluate this phenomenon.Patients and Methods:We investigated two independent cohorts of patients with advanced solid tumors treated in phase I trials with (i) programmed cell death 1 (PD-1)/PD-L1 antibodies in monotherapy or combination and (ii) targeted agents (TA) in unapproved indications. A Response Evaluation Criteria in Solid Tumors (RECIST) 1.1–based definition of hyperprogression was developed. The primary endpoint was the assessment of the rate of HPD in patients treated with ICIs or TAs using both criteria (RECIST and TGR) and the impact on overall survival (OS) in patients who achieved PD as best response.Results:Among 270 evaluable patients treated with PD-1/PD-L1 inhibitors, 29 PD-1/PD-L1–treated patients (10.7%) had HPD by RECIST definition. This group had a significantly lower OS (median of 5.23 months; 95% CI, 3.97–6.45) when compared with the non-HPD progressor group (median, 7.33 months; 95% CI, 4.53–10.12; HR = 1.73, 95% CI, 1.05–2.85; P = 0.04). In a subset of 221 evaluable patients, 14 (6.3%) were categorized as HPD using TGR criteria, differences in median OS (mOS) between this group (mOS 4.2 months; 95% IC, 2.07–6.33) and non-HPD progressors (n = 44) by TGR criteria (mOS 6.27 months; 95% CI, 3.88–8.67) were not statistically significant (HR 1.4, 95% IC, 0.70–2.77; P = 0.346). Among 239 evaluable patients treated with TAs, 26 (10.9%) were classified as having HPD by RECIST and 14 using TGR criteria in a subset of patients. No differences in OS were observed between HPD and non-HPD progressors treated with TAs.Conclusions:HPD measured by TGR or by RECIST was observed in both cohorts of patients; however, in our series, there was an impact on survival only in the immune-checkpoint inhibitor cohort when evaluated by RECIST. We propose a new way to capture HPD using RECIST criteria that is intuitive and easy to use in daily clinical practice.

Combined SPOP/ CHD1 status and time/ response to abiraterone.

Background More accurate predictive biomarkers in patients with gastroenteropancreatic neuroendocrine tumours (GEP-NETs) are needed. This study aims to investigate radiomics-based tumour phenotypes as a surrogate biomarker of the tumour vasculature and response prediction to antiangiogenic targeted agents in patients with GEP-NETs. Methods In this retrospective study, a radiomics signature was developed in patients with GEP-NETs and liver metastases receiving lenvatinib. Patients were selected from the multicentre phase II TALENT trial (NCT02678780) (development cohort). Radiomics variables were extracted from liver metastases in the pre-treatment CT-scans and selected using LASSO regression and minimum redundancy maximum relevance (mRMR). Logistic regression and Cox proportional-hazards models for radiomics and combined radiomics with clinical data were explored. The performance of the models was tested in an external cohort (test cohort). Associations between the radiomics score and vascularization factors in plasma were studied using hierarchical clustering and Mann-Whitney U test. Results A total of 89 patients were included in the study, 408 liver metastases where analysed. The CT-based radiomics signature was associated with clinical benefit in the development (training and validation sets) and test cohorts (AUC 0.75 [0.66-0.90], 0.67 [0.49-0.92] and 0.67 [0.43-0.91], respectively). The combined radiomics-clinical signature (including the radiomics score, Ki-67 index and primary tumour site) improved on radiomics-only signature performance (AUC 0.79 [95% CI 0.64-0.93]; PConclusions Radiomics-based phenotypes can provide valuable information about tumour characteristics, including vasculature, that are associated with response to antiangiogenics. Clinical Trial Registration This is a study of the Lenvatinib Efficacy in Metastatic Neuroendocrine Tumours (TALENT) phase II clinical trial (NCT02678780).

Standard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and all voxels in the region-of-interest may provide improved information, and more generalizable models. Therefore, a method of DSC-PWI analysis by means of normalized time-intensity curves point-by-point and voxel-by-voxel is constructed, and its feasibility and performance are tested in presurgical discrimination of glioblastoma and metastasis.In this retrospective study, patients with histologically confirmed glioblastoma or solitary-brain-metastases and presurgical-MR with DSC-PWI (August 2007-March 2020) were retrieved. The enhancing tumor and immediate peritumoral region were segmented on CE-T1wi and coregistered to DSC-PWI. Time-intensity curves of the segmentations were normalized to normal-appearing white matter. For each participant, average and all-voxel-matrix of normalized-curves were obtained. The 10 best discriminatory time-points between each type of tumor were selected. Then, an intensity-histogram analysis on each of these 10 time-points allowed the selection of the best discriminatory voxel-percentile for each. Separate classifier models were trained for enhancing tumor and peritumoral region using binary logistic regressions.A total of 428 patients (321 glioblastomas, 107 metastases) fulfilled the inclusion criteria (256 men; mean age, 60 years; range, 20-86 years). Satisfactory results were obtained to segregate glioblastoma and metastases in training and test sets with AUCs 0.71-0.83, independent accuracies 65-79%, and combined accuracies up to 81-88%.This proof-of-concept study presents a different perspective on brain MR DSC-PWI evaluation by the inclusion of all time-points of the curves and all voxels of segmentations to generate robust diagnostic models of special interest in heterogeneous diseases and populations. The method allows satisfactory presurgical segregation of glioblastoma and metastases.• An original approach to brain MR DSC-PWI analysis, based on a point-by-point and voxel-by-voxel assessment of normalized time-intensity curves, is presented. • The method intends to extract optimized information from MR DSC-PWI sequences by impeding the potential loss of information that may represent the standard evaluation of single concrete perfusion parameters (cerebral blood volume, percentage of signal recovery, or peak height) and values (mean, maximum, or minimum). • The presented approach may be of special interest in technically heterogeneous samples, and intrinsically heterogeneous diseases. Its application enables satisfactory presurgical differentiation of GB and metastases, a usual but difficult diagnostic challenge for neuroradiologist with vital implications in patient management.

Non-invasive differential diagnosis of brain tumours is currently based on the assessment of tumour vascularity through magnetic resonance imaging (MRI) coupled with dynamic susceptibility contrast (DSC). However, given its limited accuracy, reaching a definitive diagnosis often requires complex neurosurgical interventions that compromise the patients’ quality of life. We applied deep learning on DSC images from histology-confirmed patients with glioblastoma, metastasis or lymphoma, the three most common brain malignancies. The convolutional neural network trained on ~ 50,000 voxels from 40 patients provided intra-tumour probability maps that yielded clinical-grade diagnosis. Performance was tested in 400 additional cases and an external validation cohort (n = 128). The tool reached a three-way accuracy of 0.78, superior to standard diagnosis with cerebral blood volume (0.55) and percentage of signal recovery (0.59) perfusion metrics. Our open-access software, Brain Enhancing Region Radiological analysis (BERRY), demonstrates the potential of voxel-wise probability maps for differential diagnosis of brain tumours using standard-of-care MRI.

Colorectal cancer; Predictive markers; Tumour biomarkers Cáncer colorrectal; Marcadores predictivos; Biomarcadores tumorales Càncer colorrectal; Marcadors predictius; Biomarcadors tumorals Anti-BRAF/EGFR therapy was recently approved for the treatment of metastatic BRAFV600E colorectal cancer (mCRCBRAF-V600E). However, a large fraction of patients do not respond, underscoring the need to identify molecular determinants of treatment response. Using whole-exome sequencing in a discovery cohort of patients with mCRCBRAF-V600E treated with anti-BRAF/EGFR therapy, we found that inactivating mutations in RNF43, a negative regulator of WNT, predict improved response rates and survival outcomes in patients with microsatellite-stable (MSS) tumors. Analysis of an independent validation cohort confirmed the relevance of RNF43 mutations to predicting clinical benefit (72.7% versus 30.8%; P = 0.03), as well as longer progression-free survival (hazard ratio (HR), 0.30; 95% confidence interval (CI), 0.12–0.75; P = 0.01) and overall survival (HR, 0.26; 95% CI, 0.10–0.71; P = 0.008), in patients with MSS-RNF43mutated versus MSS-RNF43wild-type tumors. Microsatellite-instable tumors invariably carried a wild-type-like RNF43 genotype encoding p.G659fs and presented an intermediate response profile. We found no association of RNF43 mutations with patient outcomes in a control cohort of patients with MSS-mCRCBRAF-V600E tumors not exposed to anti-BRAF targeted therapies. Overall, our findings suggest a cross-talk between the MAPK and WNT pathways that may modulate the antitumor activity of anti-BRAF/EGFR therapy and uncover predictive biomarkers to optimize the clinical management of these patients. VHIO would like to acknowledge the Cellex Foundation for providing research facilities and equipment, the FERO Foundation for their funding support, the Consorcio Centro de Investigación Biomédica en Red de Cáncer (CIBERONC, CB16/12/00259) from the Institute of Health Carlos III (ISCIII), Ministry of Science and Innovation, and the Department of Health (Generalitat de Catalunya, SLT008/18/00198 and SLT008/18/00205) for their support on this research. Authors acknowledge financial support from the State Agency for Research (Agencia Estatal de Investigación) (CEX2020-001024-S / AEI / 10.13039 /501100011033). This research is funded by the SCITRON program; Novartis funded the genomics characterization by WES of samples from 28 patients from the discovery cohort and had no influence on data analysis/interpretation or writing of the paper (3003145512 to R.A.T.). S.Li. is financially supported by a Chinese Scholarship Council PhD fellowship (201909370083 to S. Li). R.P.-L. is supported by a CRIS Foundation Talent Award (TALENT19-05), the FERO Foundation, the Instituto de Salud Carlos III-Investigación en Salud (PI18/01395 and PI21/01019 to R.P.-L.) and the Prostate Cancer Foundation (Young Investigator Award). This work was supported by the Miguel Servet-I Research Award from ISCIII of the Ministry of Economy (CP17/00199 to R.A.T.), the Olga Torres Foundation Award to emerging researchers (2601 to R.A.T.), the ISCIII-FEDER (PI17/00947 and PI20/00968 to E.E.), and the Fundación AECC (CLSEN19001ELEZ to E.E.) and Ministry of Science and Innovation (Europa Redes y Gestores, ECT2020-000827 to E.E.).

Immunotherapy by immune checkpoint inhibitors has become a standard treatment strategy for many types of solid tumors. However, the majority of patients with cancer will not respond, and predicting response to this therapy is still a challenge. Artificial intelligence (AI) methods can extract meaningful information from complex data, such as image data. In clinical routine, radiology or histopathology images are ubiquitously available. AI has been used to predict the response to immunotherapy from radiology or histopathology images, either directly or indirectly via surrogate markers. While none of these methods are currently used in clinical routine, academic and commercial developments are pointing toward potential clinical adoption in the near future. Here, we summarize the state of the art in AI-based image biomarkers for immunotherapy response based on radiology and histopathology images. We point out limitations, caveats, and pitfalls, including biases, generalizability, and explainability, which are relevant for researchers and health care providers alike, and outline key clinical use cases of this new class of predictive biomarkers.

Histology; Liver; Microstructure Histología; Hígado; Microestructura Histologia; Fetge; Microestructura Purpose Relationships between diffusion-weighted MRI signals and hepatocyte microstructure were investigated to inform liver diffusion MRI modeling, focusing on the following question: Can cell size and diffusivity be estimated at fixed diffusion time, realistic SNR, and negligible contribution from extracellular/extravascular water and exchange? Methods Monte Carlo simulations were performed within synthetic hepatocytes for varying cell size/diffusivity L / D0 , and clinical protocols (single diffusion encoding; maximum b-value: {1000, 1500, 2000} s/mm2; 5 unique gradient duration/separation pairs; SNR = { ∞ , 100, 80, 40, 20}), accounting for heterogeneity in (D0,L) and perfusion contamination. Diffusion ( D ) and kurtosis ( K ) coefficients were calculated, and relationships between (D0,L) and (D,K) were visualized. Functions mapping (D,K) to (D0,L) were computed to predict unseen (D0,L) values, tested for their ability to classify discrete cell-size contrasts, and deployed on 9.4T ex vivo MRI-histology data of fixed mouse livers Results Relationships between (D,K) and (D0,L) are complex and depend on the diffusion encoding. Functions mapping (D,K) to (D0,L) captures salient characteristics of D0(D,K) and L(D,K) dependencies. Mappings are not always accurate, but they enable just under 70% accuracy in a three-class cell-size classification task (for SNR = 20, bmax = 1500 s/mm2, δ = 20 ms, and Δ = 75 ms). MRI detects cell-size contrasts in the mouse livers that are confirmed by histology, but overestimates the largest cell sizes. Conclusion Salient information about liver cell size and diffusivity may be retrieved from minimal diffusion encodings at fixed diffusion time, in experimental conditions and pathological scenarios for which extracellular, extravascular water and exchange are negligible. This project was supported by Fundació La Caixa and by the investigator-initiated PREdICT study at the Vall d'Hebron Institute of Oncology (Barcelona), funded by AstraZeneca and supporting FG. FG has received funding from the postdoctoral fellowships programme Beatriu de Pinós (2020 BP 00117), funded by the Secretary of Universities and Research (Government of Catalonia). KB is funded by a Beatriu de Pinós post-doctoral grant (2019 BP 00182). RPL is supported by a CRIS Foundation Talent Award (TALENT19-05), the Instituto de Salud Carlos III-Investigación en Salud (PI18/01395), Spanish Ministry for Science, Innovation and Universities (RTI2018-095209-B-C21, FIS-G64384969), Prostate Cancer Foundation Young Investigator Award and Fero Foundation. ICS is supported by a fellowship from Fundació ”la Caixa” (ID 100010434) and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 847648, fellowship code LCF/BQ/PI20/1176003

Anti-BRAF/EGFR therapy was recently approved for the treatment of metastatic BRAFsupV600E/supcolorectal cancer (mCRCsupBRAF-V600E/sup). However, a large fraction of patients do not respond, underscoring the need to identify molecular determinants of treatment response. Using whole-exome sequencing in a discovery cohort of patients with mCRCsupBRAF-V600E/suptreated with anti-BRAF/EGFR therapy, we found that inactivating mutations in RNF43, a negative regulator of WNT, predict improved response rates and survival outcomes in patients with microsatellite-stable (MSS) tumors. Analysis of an independent validation cohort confirmed the relevance of RNF43 mutations to predicting clinical benefit (72.7% versus 30.8%; P = 0.03), as well as longer progression-free survival (hazard ratio (HR), 0.30; 95% confidence interval (CI), 0.12-0.75; P = 0.01) and overall survival (HR, 0.26; 95% CI, 0.10-0.71; P = 0.008), in patients with MSS-RNF43supmutated/supversus MSS-RNF43supwild-type/suptumors. Microsatellite-instable tumors invariably carried a wild-type-like RNF43 genotype encoding p.G659fs and presented an intermediate response profile. We found no association of RNF43 mutations with patient outcomes in a control cohort of patients with MSS-mCRCsupBRAF-V600E/suptumors not exposed to anti-BRAF targeted therapies. Overall, our findings suggest a cross-talk between the MAPK and WNT pathways that may modulate the antitumor activity of anti-BRAF/EGFR therapy and uncover predictive biomarkers to optimize the clinical management of these patients.

3560 Background: Currently, there are no well-established biomarkers available to select mCRC patients (pts) who will benefit most from antiangiogenic therapy. RAS mutant (mt) allele fraction in plasma (plMAF) is an independent prognostic factor in mCRC. Preliminary data from our group suggests the possible predictive role of plMAF in RAS mt pts treated with 1st line chemotherapy (ct) +/- bevacizumab (bev). Methods: Data prospectively/retrospectively collected from RASmt mCRC pts who received 1st line ct+/-bev treatment in our center, selecting the subset of pts with plMAF sample evaluable with digital PCR (BEAMing) at baseline. Pts were stratified as high (≥ 5.8%) or low ( < 5.8%) plMAF, based on a previously established prognostic cutoff (Elez et al, Mol Onc 2019). We investigated the associations between clinic-pathological variables, overall survival (OS), and progression-free survival (PFS) stratified by plMAF RAS levels using Cox regression models. OS and PFS were calculated by Kaplan-Meier method. Murine PDX were developed from mCRC pts including models from patients with KRASG12/G13 mutations to explore recapitulation of the clinical findings. Results: From October ‘17 to December ‘21, 102 basal plasma samples were analyzed by BEAMing. 47 pts (46%) were classified as high, 39 pts (38%) as low, and 16 (16%) as no-mt detected by BEAMing (non-shedding). OS was significant longer in low plMAF pts than in high plMAF pts (median OS 15.9 vs 37.1 months (mo); HR 0.43; p = 0.001). In high plMAF pts, a trend towards a better PFS was observed in those pts treated with ct+bev compared to ct alone (median 9.4 vs 6.1 mo; HR 0.6; p = 0.18). No differences were observed in low plMAF pts treated with ct +/- bev (median 14.5 vs 14.9 mo; HR 1.2; p = 0.58). Results were not modified when adjusted by the presence of liver metastases. The multivariate PFS model showed no association between RAS plMAF and clinicopathological variables, except for treatment benefit with ct+bev and better outcomes in pts with resectable liver metastases. Interestingly, human ctDNA in one murine PDX model from non-shedding pt was not detectable, whereas human ctDNA was present in another PDX model with a plMAF of 36.2%. Conclusions: This study suggests that plMAF could be a promising predictive biomarker of response to bev in 1st line RASmt mCRC, but more pts need to be analyzed to confirm this effect. Our results confirm the prognostic role of RASmt plMAF in mCRC. plMAF could partially depend on tumor cell shedding degree and characteristics on the tumor vasculature architecture, this is being investigated in ongoing imaging studies and PDX models. These models will also help to understand the biology behind tumor response to bev and its connection with ctDNA shedding.

1086 Background: Genomic alterations driving MBC progression may be better captured by ctDNA reflecting clonal evolution, but it is currently unknow whether ctDNA analysis can replace tumor sequencing for clinical decision purposes. Aim: to study the concordance between mut in synchronous plasma and tumor samples prospectively collected from patients (pts) with MBC progressing on their last systemic therapy. Methods: MiSeq Amplicon-based NGS (custom panel of 60 cancer-related genes; BRCA1/2 and PALB2 not included) was performed in both tumor biopsy and plasma. The concordance of ESCAT Tier I and II mut ( PIK3CA, AKT1, ERBB2, ESR1, PTEN) was determined and correlated with mutant allele fraction (MAF), TDV, and clinical features. Findings from liquid biopsies were classified as true positive (TP-ctDNA) if a given mut was detected in both tumor and plasma and false negative (FN-ctDNA) if only in the tumor. TDV: all metastasis volume assessed by CT scan (excluding sclerotic bone metastasis), and analyzed by an experienced radiologist using the 3DSlicer semiautomatic segmentation tool (TDV = pixel size x number of pixels). Non-shedding cases were those where any mut was detected in tumor but none in plasma. Results: 88 cases were collected (luminal 64, HER2+ 17, triple negative 7). Median age at diagnosis 49 years (range 28-80). Radiomics assessment could be performed in 78/88 cases. The plasma/tissue concordance at case level was 74%. Discordance came from 23 cases; in 15 cases mut was only found in tissue and in 8 cases it was only detected in plasma. At gene level, PIK3CA had the highest concordance (79%); in ESR1 it was 52%. Higher concordance associated with non-luminal subtype (OR 0.08, 95%CI 0.002 – 0.59) and shorter interval between primary diagnosis and metastatic relapse (20.3 vs 51 months; p =.02), but not with MAF. FN-ctDNA occurred in 15/49 cases (31%) and associated with luminal subtype (p =.02), but not with other clinical variables. Non-shedding cases associated with older age (p =.03), luminal subtype (p =.007), low TDV (p =.0006) and < 3 metastatic sites (p =.05). In patients with visceral metastasis (n = 45), higher TDV associated with lower probability of FN-ctDNA (p =.03). All non-luminal subtypes were shedders and all but one were TP-ctDNA. In multivariate analysis, higher probability of TP-ctDNA in luminal tumors associated with tumor sampling from a progressing lesion (OR 10.8; 95% 1.5 – 122; p =.03) and shorter interval between diagnosis of metastatic disease and biopsy (OR 0.96, 95% CI 0.92 – 0.99; p =.03). Conclusions: Our results suggest that ctDNA can detect a significant proportion of clinically relevant mut in MBC. Patients’ characteristics, tumor subtype, type of gene, and tumor volume should be integrated with ctDNA results to better inform clinical decisions.

TPS209 Background: Multiple lines of evidence suggest a crosstalk between androgen receptor (AR) signaling and DNA damage repair (DDR) in prostate cancer. Co-targeting both pathways in mHNPC can result in a clinically relevant synergistic effect. ZZFIRST trial is evaluating the combination of TALA –a poly(ADP-ribose) polymerase inhibitor– and EZ –an AR signaling inhibitor– in mHNPC patients. Methods: This is a multicenter, open-label, randomized, investigator-initiated phase 2 clinical trial. Men aged ≥18 years with histologically confirmed mHNPC, an ECOG performance status of 0-1, and a prostate-specific antigen (PSA) ≥4 ng/mL at enrolment are eligible. Patients must have not received previous systemic treatment for locally advanced or mHNPC. A total of 54 patients will start treatment with EZ 160 mg/day for 2 28-day cycles in addition to standard androgen-deprivation therapy (ADT). Patients are then randomized and stratified based on homologous recombination gene alterations on a 1:2 ratio to either continue EZ 160 mg/day, or to receive EZ 160 mg/day plus TALA 0.5 mg/day. In both arms, patients will continue ADT throughout the trial. Treatment will continue until progressive disease (PD) or unacceptable toxicity. PSA will be determined every 4 weeks and radiological tumor extend will be assessed at screening and every 8 weeks for the 6 initial months of treatment and every 12 weeks thereafter until PD. Primary endpoint is PSA-complete response defined as the percentage of patients with PSA < 0.2 ng/mL at 12 months of therapy. Secondary endpoints include PSA-complete response at any time point and at month 7, PSA response ( < 4 ng/ml) at 7 and 12 months, PSA-progression-free survival (PSA-PFS), radiologic PFS, time to castration resistance based on PSA-PFS and rPFS, and overall survival. Safety will be assessed as per NCI-CTCAE 5.0. Exploratory endpoints include analysis of transcriptional changes in AR and DDR pathways and assessment of genomic signatures on tumor and liquid biopsies collected at baseline, 4 weeks, and PD. An imaging sub-study of whole-body diffusion weighted MRI will help to further study antitumor activity and drug resistance mechanisms. This trial was opened to accrual in July 2020. Currently 44 patients have been enrolled (with 37 randomized by cycle 3 day 1) out of 54 expected. Analysis will be assessed with the exact binomial test. At least 11 patients must maintain PSA < 0.2 ng/mL by 12 months of therapy among 32 evaluable patients in the combination arm to justify further investigation of this strategy. A drop-out rate of 10% has been considered. Clinical trial information: NCT04332744.