Precision Oncology Trials Research Articles

Assessing actionability and incidental findings of germline variants in two precision oncology trials

IntroductionHigh-throughput sequencing techniques have revolutionized oncology. Paired germline-tumor DNA analysis has emerged as a comprehensive strategy to uncover actionable alterations in advanced cancer patients (ACP) enrolled in precision oncology trials. However, challenges persist in variant interpretation and managing incidental germline findings. MethodsWe conducted a study involving 288 ACP from MOSCATO (NCT01566019) and MATCHR (NCT02517892) trials to assess germline variants impacting cancer-related genes. Germline DNA sequencing was performed using a panel of 250 cancer-related genes, and the results were discussed during tumor molecular board sessions. ResultsGermline pathogenic variants (PV) were classified according to the ESCAT classification. Lung cancer (36.8 %), followed by prostate (18.4 %) and breast cancer (17.7 %), comprised the most prevalent tumor types. PVs were found in 12.5 % of patients. Most PVs were classified as ESCAT X (63.9 %), highlighting limited therapeutic actionability. Notably,2 % of patients had actionable variants (ESCAT I-A/II-A). Incidental findings included 7.3 % of patients with PVs in cancer-predisposition genes, with 2.4 % having very high-risk potential, necessitating mandatory oncogenetic counseling. Nearly one in five patients (21.9 %) had at least one VUS. DiscussionOur study underscores the significance of germline sequencing in identifying actionable alterations and the need for improved variant interpretation as well as pretest counseling plans in precision oncology trials.

Molecular profiling of 888 pediatric tumors informs future precision trials and data-sharing initiatives in pediatric cancer

To inform clinical trial design and real-world precision pediatric oncology practice, we classified diagnoses, assessed the landscape of mutations, and identified genomic variants matching trials in a large unselected institutional cohort of solid tumors patients sequenced at Dana-Farber / Boston Children’s Cancer and Blood Disorders Center. Tumors were sequenced with OncoPanel, a targeted next-generation DNA sequencing panel. Diagnoses were classified according to the International Classification of Diseases for Oncology (ICD-O-3.2). Over 6.5 years, 888 pediatric cancer patients with 95 distinct diagnoses had successful tumor sequencing. Overall, 33% (n = 289/888) of patients had at least 1 variant matching a precision oncology trial protocol, and 14% (41/289) were treated with molecularly targeted therapy. This study highlights opportunities to use genomic data from hospital-based sequencing performed either for research or clinical care to inform ongoing and future precision oncology clinical trials. Furthermore, the study results emphasize the importance of data sharing to define the genomic landscape and targeted treatment opportunities for the large group of rare pediatric cancers we encounter in clinical practice.

Organoids for Functional Precision Medicine in Advanced Pancreatic Cancer

BackgroundPatient-derived organoids (PDO) are promising tumor avatars that could enable ex vivo drug tests to personalize patients’ treatment in the frame of functional precision oncology (FPM). Yet, clinical evidence remain scarce. This study aims to evaluate whether PDO can be implemented in clinical practice to benefit patients with advanced refractory pancreatic adenocarcinoma (PDAC). MethodsDuring 2021-2022, 87 patients were prospectively enrolled in an IRB-approved protocol. Inclusion criteria were: histologically-confirmed PDAC, tumor site accessible. A panel of 25 approved antitumor therapies (chemogram) was tested and compared to patient responses to assess PDO predictive values and map the drug sensitivity landscape in PDAC. ResultsFifty-four PDOs were generated from 87 pretreated patients (take-on rate 62%). The main PDO mutations were KRAS (96%), TP53 (88%) and CDKN2A/B (22%), with 91% concordance rate with their tumor of origin. The mean turnaround-time to chemogram was 6.8 weeks. In 91% of cases, ≥1 hit was identified (gemcitabine (n=20/54), docetaxel (n=18/54) and vinorelbine (n=17/54) with a median of 3 hits/patient [range:0-12]).Our cohort included 34 evaluable patients with full clinical follow-up. We report a chemogram sensitivity of 83.3% and specificity of 92.9%. The overall-response rate and progression-free survival were higher when patients received a “hit” treatment as compared to patients that received a “non-hit” drug (as part of routine management).Finally, we leveraged our PDO collection as a platform for drug validation and combo identification. We tested the anti-KRASG12D (MRTX1133), alone or combined, and identified a specific synergy with anti-EGFR therapies in KRASG12D variants. ConclusionWe report the largest prospective study aiming at implementing PDO-based FPM and identify very robust predictive values in this clinical setting. In a clinically relevant turnaround-time, we identify putative hits for 91% of patients, providing unexpected potential survival benefits in this very aggressive indication. While this remains to be confirmed in interventional precision oncology trials, PDO collection already provide powerful opportunities for drugs and combinatorial treatment development.

Patient eligibility for trials with imaging response assessment at the time of molecular tumor board presentation

PurposeTo assess the eligibility of patients with advanced or recurrent solid malignancies presented to a molecular tumor board (MTB) at a large precision oncology center for inclusion in trials with the endpoints objective response rate (ORR) or duration of response (DOR) based on Response Evaluation Criteria in Solid Tumors (RECIST version 1.1).MethodsProspective patients with available imaging at the time of presentation in the MTB were included. Imaging data was reviewed for objectifiable measurable disease (MD) according to RECIST v1.1. Additionally, we evaluated the patients with MD for representativeness of the identified measurable lesion(s) in relation to the overall tumor burden.Results262 patients with different solid malignancies were included. 177 patients (68%) had MD and 85 (32%) had non-measurable disease (NMD) at the time point of MTB presentation in accordance with RECIST v1.1. MD was not representative of the overall tumor burden in eleven patients (6%). The main reasons for NMD were lesions with longest diameter shorter than 10 mm (22%) and non-measurable peritoneal carcinomatosis (18%). Colorectal cancer and malignant melanoma displayed the highest rates of MD (> 75%). In contrast, gastric cancer, head and neck malignancies, and ovarian carcinoma had the lowest rates of MD (< 55%). In case of MD, the measurable lesions were representative of the overall tumor burden in the vast majority of cases (94%).ConclusionApproximately one third of cancer patients with advanced solid malignancies are not eligible for treatment response assessment in trials with endpoints ORR or DOR at the time of MTB presentation. The rate of patients eligible for trials with imaging endpoints differs significantly based on the underlying malignancy and should be taken under consideration during the planning of new precision oncology trials.

Precision oncology clinical trials: A systematic review of phase II clinical trials with biomarker-driven, adaptive design.

e23005 Background: Novel clinical trial designs are conducted in the precision medicine era. This study aimed to evaluate biomarker-driven, adaptive phase II trials in precision oncology, focusing on infrastructure, efficacy, and safety. Methods: We systematically reviewed and analyzed the target studies. EMBASE and PubMed searches from 2015 to 2023 generated 29 eligible trials. Data extraction included infrastructure, biomarker screening methodologies, efficacy, and safety profiles. Results: Government agencies, cancer hospitals, and academic societies with accumulated experiences led investigator-initiated precision oncology clinical trials (IIPOCTs), which later guided sponsor-initiated precision oncology clinical trials (SIPOCTs). Most SIPOCTs were international studies with basket design. IIPOCTs primarily used the central laboratory for biomarker screening, but SIPOCTs used both central and local laboratories. Most of the studies adapted next-generation sequencing and/or immunohistochemistry for biomarker screening. Fifteen studies included an independent central review committee for outcome investigation. Efficacy assessments predominantly featured objective response rate as the primary endpoint, with varying results. Nine eligible studies contributed to the United States Food and Drug Administration’s marketing authorization. Safety monitoring was rigorous, but reporting formats lacked uniformity. Health-related quality of life and patient-reported outcomes were described in some protocols but rarely reported. Table. Precision Oncology Trials led to the FDA approval. Conclusions: Our results reveal that precision oncology trials with adaptive design rapidly and efficiently evaluate anticancer drugs’ efficacy and safety, particularly in specified biomarker-driven cohorts. The evolution from IIPOCT to SIPOCT has facilitated fast regulatory approval, providing valuable insights into the precision oncology landscape. [Table: see text]

Precision Oncology Clinical Trials: A Systematic Review of Phase II Clinical Trials with Biomarker-Driven, Adaptive Design.

Novel clinical trial designs are conducted in the precision medicine era. This study aimed to evaluate biomarker-driven, adaptive phase II trials in precision oncology, focusing on infrastructure, efficacy, and safety. We systematically reviewed and analyzed the target studies. EMBASE and PubMed searches from 2015 to 2023 generated 29 eligible trials. Data extraction included infrastructure, biomarker screening methodologies, efficacy, and safety profiles. Government agencies, cancer hospitals, and academic societies with accumulated experiences led investigator-initiated precision oncology clinical trials (IIPOCTs), which later guided sponsor-initiated precision oncology clinical trials (SIPOCTs). Most SIPOCTs were international studies with basket design. IIPOCTs primarily used the central laboratory for biomarker screening, but SIPOCTs used both central and local laboratories. Most of the studies adapted next-generation sequencing and/or immunohistochemistry for biomarker screening. Fifteen studies included an independent central review committee for outcome investigation. Efficacy assessments predominantly featured objective response rate as the primary endpoint, with varying results. Nine eligible studies contributed to the United States Food and Drug Administration's marketing authorization. Safety monitoring was rigorous, but reporting formats lacked uniformity. Health-related quality of life and patient-reported outcomes were described in some protocols but rarely reported. Our results reveal that precision oncology trials with adaptive design rapidly and efficiently evaluate anticancer drugs' efficacy and safety, particularly in specified biomarker-driven cohorts. The evolution from IIPOCT to SIPOCT has facilitated fast regulatory approval, providing valuable insights into the precision oncology landscape.

Precision Endpoints for Contemporary Precision Oncology Trials.

Traditional endpoints such as progression-free survival and overall survival do not fully capture the pharmacologic and pharmacodynamic effects of a therapeutic intervention. Incorporating mechanism-driven biomarkers and validated surrogate proximal endpoints can provide orthogonal readouts of anti-tumor activity and delineate the relative contribution of treatment components on an individual level, highlighting the limitation of solely relying on aggregated readouts from clinical trials to facilitate go/no-go decisions for precision therapies.

Abstract 951: 3D-PREDICT - An ex vivo precision oncology and clinical trial enrichment platform by Nilogen Oncosystems

Abstract Background: Immuno-oncology (IO) therapy with checkpoint inhibitors has demonstrated a higher response rate, duration of response, and overall survival than chemotherapy. However, predicting which FDA approved IO therapies or pharmaceutical agents, as well as best combinations and delivery sequence, will have optimal patient response remains a significant challenge for personalized medicine. Nilogen Oncosystem’s 3D-PREDICT is a novel ex vivo therapeutic investigative platform that provides a functional model of a patient’s tumor to investigate susceptibility to different therapeutic approaches directly. Derived from core biopsies, the 3D-PREDICT model captures the tumor heterogeneity while preserving the tumor microenvironment by retaining stromal components and cell-cell and cell-extracellular matrix interactions. Here, we employed the 3D-PREDICT platform to compare the efficacy of different immunotherapeutic approaches in various solid tumor indications ex vivo. Methods: Tumoroids from 18-gauge core biopsies from solid tumors were generated using Nilogen Oncosystems’s proprietary mechanical process forgoing any enzymatic digestion or propagation while keeping the spatial contexture of stroma intact. Pooled tumoroids were treated ex vivo for 72 hours with various standard-of-care mono or combo therapy (carboplatin, anti-programmed death-ligand 1 (PD-L1) antibody atezolizumab, anti-CD47 antibody magrolimab, anti-programmed cell death receptor-1 (PD-1) antibody nivolumab, or a combination of magrolimab and atezolizumab). Treatment-mediated immune modulation in tumor microenvironment were analyzed to predict patient response to subjected therapies. Results: Treatment-mediated tumor cell killing activity was evaluated using 3D high throughput confocal imaging and Nilogen Oncosystems’s proprietary algorithm for data analysis. The ex vivo response to checkpoint inhibitors was correlated with retrospective patient clinical information. Tumoroid samples treated with atezolizumab and magrolimab demonstrated enhanced tumor cell killing. Approximately 20% of tumors showed increased CD8 T-cell activation upon ex vivo treatment, correlating with proinflammatory cytokine release in conditioned media. Conclusions: The 3D-PREDICT ex vivo tumoroid platform provides a unique resource to predict patients’ response to treatment, potentially serving as a clinical diagnostic to guide clinical care and stratifying patient procurement into clinical trials. This would allow prospective assessment of therapeutic efficacy ex vivo to match each patient to the most effective and least toxic, to improving outcomes, reducing costs, and assigning therapeutics on a more rational basis. Citation Format: Jared Ehrhart, Seth Currlin, Michelle Ataya, Alliyah Humphrey, Rikhia Chakraborty. 3D-PREDICT - An ex vivo precision oncology and clinical trial enrichment platform by Nilogen Oncosystems [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 951.

Abstract 907: ScreenDL: A transfer learning framework integrating tumor omics and functional drug screening for personalized clinical drug response prediction

Abstract Precision oncology hinges on accurate prediction of patient-specific treatment response from tumor molecular inputs. While deep learning (DL) models achieve state-of-the-art response prediction in cell lines, existing methods do not readily translate to the clinic where training data is limited. Here, we have developed and validated ScreenDL, a novel DL framework designed explicitly for use in clinical precision oncology applications. The underlying architecture of ScreenDL consists of two fully connected branches dedicated to learning rich embeddings of a drug’s chemical structure and a tumor’s transcriptomic profile respectively. These tumor omic and drug molecular embeddings are fed into a shared response prediction subnetwork which outputs predicted ln(IC50) values. The ScreenDL training schema incorporates three phases, each designed to improve clinical response prediction: (1) an initial pretraining phase leverages large-scale cell line pharmacogenomic datasets to extract patterns/relationships linking tumor omics and drug response; (2) subsequent transfer learning integrates pharmacogenomic data from patient-derived models of cancer, adapting the pretrained model to a more patient-relevant context; (3) patient-level omics and drug screening data (e.g., from matched patient-derived organoid (PDO) models) is integrated through patient-specific fine-tuning, generating a patient-specific response prediction model. Importantly, biomaterial from surgical tumor resection is often available for organoid establishment and patient-specific drug screening in practical clinical scenarios. To assess the utility of our ScreenDL framework for clinical response prediction, we applied ScreenDL to predict treatment response in 50 advanced/metastatic triple negative breast cancer (TNBC) patient-derived xenograft models (PDX). We leveraged tumor omics profiles for each PDX and drug screening data from matched PDX-derived organoids (PDxOs), mirroring the combination of patient-level tumor omic characterization and functional drug screening in matched PDOs, a protocol currently being piloted in Functional Precision Oncology trials at the University of Utah Huntsman Cancer Institute. After cell line pretraining, ScreenDL achieves a modest median Pearson correlation per drug of 0.15 relative to 0.03 for the leading compared model. However, transfer learning and subsequent PDX-specific fine-tuning significantly improve performance, producing median Pearson correlations per drug of 0.39 and 0.51, respectively. These results demonstrate the ability of our ScreenDL framework to dramatically improve response prediction in clinically relevant domains, bringing DL-based precision treatment selection closer to clinical application. Citation Format: Casey Sederman, Tonya Di Sera, Yi Qiao, Xiaomeng Huang, Bryan E. Welm, Alana L. Welm, Gabor Marth. ScreenDL: A transfer learning framework integrating tumor omics and functional drug screening for personalized clinical drug response prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 907.

Infigratinib versus placebo in patients with resected urothelial cancer (UC) bearing FGFR3 mutation or fusion: Primary DFS analysis from the phase 3, randomized PROOF302 study.

629 Background: In the subset of patients with UC bearing alterations in the fibroblast growth factor 3 ( FGFR3) gene, targeted therapies directed at this moiety have shown substantial antitumor effect in the metastatic setting. We examine infigratinib, a potent and selective inhibitor of FGFR3, as adjuvant therapy in patients with high-risk resected UC. Methods: In an international multicenter phase III clinical trial, we randomly assigned patients in a 1:1 ratio to receive either oral infigratinib (125 mg) or placebo daily for 21 days of a 28-day treatment cycle, for a maximum of 13 cycles or until disease recurrence. Eligible patients had confirmed invasive UC with susceptible FGFR3 alterations and had undergone radical surgery within 120 days of randomization. The primary endpoint of the study was centrally assessed disease-free survival (DFS), with secondary endpoints including investigator-assessed DFS, metastasis-free survival (MFS) and overall survival (OS). Results: Despite intensive efforts to enroll approximately 218 patients (with 822 patients consented to molecular pre-screening), only 39 patients were enrolled with 20 and 19 patients randomized to receive infigratinib and placebo, respectively. The frequency of FGFR3 alteration was significantly lower than anticipated, occurring in only 19% of patients overall; mutations were observed in 13% of patients with lower tract UC and 30% of patients with upper tract UC. No significant differences were observed in DFS, MFS or OS, and more frequent grade 3/4 adverse events were noted in the experimental arm (35% versus 16%). No fatal adverse events were observed. Conclusions: Our failure to accrue sufficient patients to the current trial precludes any definitive conclusions around the role of infigratinib as adjuvant therapy for FGFR3-altered UC. In the process of study conduct, however, we garnered substantial insights that may aid in the development of future precision oncology trials for adjuvant UC. Clinical trial information: NCT04197986 .

Framework for the Use of External Controls to Evaluate Treatment Outcomes in Precision Oncology Trials.

Advances in genomics have enabled anticancer therapies to be tailored to target specific genomic alterations. Single-arm trials (SATs), including those incorporated within umbrella, basket, and platform trials, are widely adopted when it is not feasible to conduct randomized controlled trials in rare biomarker-defined subpopulations. External controls (ECs), defined as control arm data derived outside the clinical trial, have gained renewed interest as a strategy to supplement evidence generated from SATs to allow comparative analysis. There are increasing examples demonstrating the application of EC in precision oncology trials. The prospective application of EC in conducting comparative studies is associated with distinct methodological challenges, the specific considerations for EC use in biomarker-defined subpopulations have not been adequately discussed, and a formal framework is yet to be established. In this review, we present a framework for conducting a prospective comparative analysis using EC. Key steps are (1) defining the purpose of using EC to address the study question, (2) determining if the external data are fit for purpose, (3) developing a transparent study protocol and a statistical analysis plan, and (iv) interpreting results and drawing conclusions on the basis of a prespecified hypothesis. We specify the considerations required for the biomarker-defined subpopulations, which include (1) specifying the comparator and biomarker status of the comparator group, (2) defining lines of treatment, (3) assessment of the biomarker testing panels used, and (4) assessment of cohort stratification in tumor-agnostic studies. We further discuss novel clinical trial designs and statistical techniques leveraging EC to propose future directions to advance evidence generation and facilitate drug development in precision oncology.

OncoCTMiner: streamlining precision oncology trial matching via molecular profile analysis.

By establishing omics sequencing of patient tumors as a crucial element in cancer treatment, the extensive implementation of precision oncology necessitates effective and prompt execution of clinical studies for approving molecular-targeted therapies. However, the substantial volume of patient sequencing data, combined with strict clinical trial criteria, increasingly complicates the process of matching patients to precision oncology studies. To streamline enrollment in these studies, we developed OncoCTMiner, an automated pre-screening platform for molecular cancer clinical trials. Through manual tagging of eligibility criteria for 2227 oncology trials, we identified key bio-concepts such as cancer types, genes, alterations, drugs, biomarkers and therapies. Utilizing this manually annotated corpus along with open-source biomedical natural language processing tools, we trained multiple named entity recognition models specifically designed for precision oncology trials. These models analyzed 460 952 clinical trials, revealing 8.15 million precision medicine concepts, 9.32 million entity-criteria-trial triplets and a comprehensive precision oncology eligibility criteria database. Most significantly, we developed a patient-trial matching system based on cancer patients' clinical and genetic profiles, which can seamlessly integrate with the omics data analysis platform. This system expedites the pre-screening process for potentially suitable precision oncology trials, offering patients swifter access to promising treatment options. Database URL https://oncoctminer.chosenmedinfo.com.

Promoting informed approaches in precision oncology and clinical trial participation for Black patients with cancer: Community-engaged development and pilot testing of a digital intervention.

Black patients with cancer are less likely to receive precision cancer treatments than White patients and are underrepresented in clinical trials. To address these disparities, the study aimed to develop and pilot-test a digital intervention to improve Black patients' knowledge about precision oncology and clinical trials, empower patients to increase relevant discussion, and promote informed decision-making. A community-engaged approach, including a Community Advisory Board and two rounds of key informant interviews with Black patients with cancer, their relatives, and providers (n=48) was used to develop and refine the multimedia digital intervention. Thematic analysis was conducted for qualitative data. The intervention was then pilot-tested with 30 Black patients with cancer to assess feasibility, acceptability, appropriateness, knowledge, decision self-efficacy, and patient empowerment; Wilcoxon matched pairs signed-rank test was used to analyze quantitative data. The digital tool was found to be feasible, acceptable, and culturally appropriate. Key informants shared their preferences and recommendations for the digital intervention and helped improve cultural appropriateness through user and usability testing. In the pilot test, appreciable improvement was found in participants' knowledge about precision oncology (z=-2.04, p=.052), knowledge about clinical trials (z=-3.14, p=.001), and decisional self-efficacy for targeted/immune therapy (z=-1.96, p=.0495). The digital intervention could be a promising interactive decision-support tool for increasing Black patients' participation in clinical trials and receipt of precision treatments, including immunotherapy. Its use in clinical practice may reduce disparities in oncology care and research. We developed a digital interactive decision support tool for Black patients with cancer by convening a Community Advisory Board and conducting interviews with Black patients with cancer, their relatives, and providers. We then pilot-tested the intervention with newly diagnosed Black patients with cancer and found appreciable improvement in participants' knowledge about precision oncology, knowledge about clinical trials, and confidence in making decisions for targeted/immune therapy. Our digital tool has great potential to be an affordable and scalable solution for empowering and educating Black patients with cancer to help them make informed decisions about precision oncology and clinical trials and ultimately reducing racial disparities.

Signaling-induced systematic repression of miRNAs uncovers cancer vulnerabilities and targeted therapy sensitivity

Targeted therapies are effective in treating cancer, but success depends on identifying cancer vulnerabilities. In our study, we utilize small RNA sequencing to examine the impact of pathway activation on microRNA (miRNA) expression patterns. Interestingly, we discover that miRNAs capable of inhibiting key members of activated pathways are frequently diminished. Building on this observation, we develop an approach that integrates a low-miRNA-expression signature to identify druggable target genes in cancer. We train and validate our approach in colorectal cancer cells and extend it to diverse cancer models using patient-derived invitro and invivo systems. Finally, we demonstrate its additional value to support genomic and transcriptomic-based drug prediction strategies in a pan-cancer patient cohort from the National Center for Tumor Diseases (NCT)/German Cancer Consortium (DKTK) Molecularly Aided Stratification for Tumor Eradication (MASTER) precision oncology trial. In conclusion, our strategy can predict cancer vulnerabilities with highsensitivity and accuracy and might be suitable for future therapy recommendations in a variety of cancer subtypes.

Travel Time and Distance and Participation in Precision Oncology Trials at the National Cancer Center Hospital

Genotype-matched trials, which are becoming increasingly important in the precision oncology era, require referrals from institutions providing comprehensive genomic profiling (CGP) testing to those conducting these trials, and the travel burden for trial participation is significant. However, it remains unknown whether travel time or distance are associated with genotype-matched trial participation. To assess whether travel time or distance are associated with disparities in genotype-matched trial participation following CGP testing. This retrospective cohort study from June 2020 to June 2022 included patients with advanced or metastatic solid tumors referred to the National Cancer Center Hospital for participation in genotype-matched trials following CGP testing and discussion by molecular tumor boards. Data were analyzed from June to October 2022. Travel time and distance. The primary and secondary outcomes were enrollment in genotype-matched trials and all-cancer clinical trials, respectively. Of 1127 patients (mean [range] age, 62 [16-85] years; 584 women [52%]; all residents of Japan), 127 (11%) and 241 (21%) were enrolled in genotype-matched trials and all-cancer clinical trials, respectively. The overall median (IQR) travel distance and time were 38 (21-107) km and 55 (35-110) minutes, respectively. On multivariable regression with 23 covariates, travel distance (≥100 km vs <100 km) was not associated with the likelihood of genotype-matched trial participation (26 of 310 patients [8%] vs 101 of 807 patients [12%]; odds ratio [OR], 0.64; 95% CI, 0.40-1.02), whereas in patients with travel time of 120 minutes or more, the likelihood of genotype-matched trial participation was significantly lower than those with travel time less than 120 minutes (19 of 276 patients [7%] vs 108 of 851 patients [13%]; OR, 0.51; 95% CI, 0.29-0.84). The likelihood of genotype-matched trial participation decreased as travel time increased from less than 40 (38 of 283 patients [13%]) to 40 to 120 (70 of 568 patients [12%]) and 120 or more (19 of 276 patients [7%]) minutes (OR, 0.74; 95% CI, 0.48-1.17; OR, 0.41; 95% CI, 0.22-0.74, respectively). Neither travel time nor distance were associated with the likelihood of all-cancer clinical trial participation. In this cohort study of patients undergoing CGP testing, an increased travel time was associated with a decreased likelihood of genotype-matched trial participation. This warrants further research on interventions, such as decentralization of clinical trials to mitigate travel burden.

Clinical activity of palbociclib and ribociclib monotherapy in advanced cancers with cyclin D-CDK4/6 pathway alterations in the Dutch DRUP and Australian MoST trials.

3101 Background: The Dutch Drug Rediscovery Protocol (DRUP, NCT02925234) and the Australian Cancer Molecular Screening and Therapeutic (MoST, ACTRN12616000931471) Program are similar non-randomized, multi-drug, pan-cancer trial platforms that aim to identify signals of clinical activity of molecularly matched targeted therapies or immunotherapies outside their approved indications. Here, we report results for advanced or metastatic pre-treated cancer patients with tumors harboring Cyclin D-CDK4/6 pathway alterations that were treated with CDK4/6 inhibitors palbociclib or ribociclib as monotherapy. Methods: Eligible adult patients had therapy-refractory solid malignancies with one of the following alterations: amplifications of CDK4, CDK6, CCND1, CCND2 or CCND3, or complete loss of CDKN2A or SMARCA4. Within MoST, all patients were treated with palbociclib, whereas in DRUP, palbociclib and ribociclib were assigned to different cohorts (defined by tumor type and alteration). The primary endpoints for this combined analysis were clinical benefit, defined as confirmed objective response or stable disease (SD) ≥16 weeks, and safety. Results: Among 139 enrolled patients 33 tumor types were represented, with glioblastoma multiforme (18%), sarcoma (13%), non-small cell lung cancer (9%) and pancreatic cancers (9%) being most common; 116 patients received palbociclib and 23 received ribociclib. In 112 evaluable patients, the objective response rate was 0% and clinical benefit rate at 16 weeks was 15% (17 cases of SD ≥16 weeks). Median progression-free survival was 4 months (95% CI 3-5 months) and median overall survival 5 months (95% CI 4-6 months). No unexpected toxicity was observed for either drug. Additional driver alterations were identified in 98% and 77% of patients based on whole genome sequencing (WGS) and sequencing next generation sequencing (NGS) analysis at baseline, respectively (Most common in WGS: TP53 (45%), KRAS (25%), PTEN (15%), SMAD4 (15%). Most common in NGS: TP53 (21%), EGFR (17%), TERT promoter (15%), KRAS (11%). Conclusions: Results show minimal clinical activity of palbociclib and ribociclib monotherapy in patients with pre-treated cancers harboring cyclin D-CDK4/6 pathway alterations. Our findings indicate that single agent use of palbociclib or ribociclib is not recommended. Furthermore, we demonstrate that merging data from similar precision oncology trials is feasible. This finding is especially relevant in the context of precision oncology, with rare molecular subsets and/or cancer histologies. Clinical trial information: NCT02925234 .

Pilot study of a micro-organosphere drug screen platform to lead care in advanced breast cancer (MODEL-ABC).

1107 Background: ASCO guidelines suggest using single agent chemotherapy for patients with advanced breast cancer (ABC). Single agent chemotherapy provides modest response rates in ABC, causing patients to be exposed to unnecessary toxicity without benefit. Thus, there is an unmet clinical need to develop a clinically applicable assay to guide treatment. We recently reported the use of MicroOrganoSpheres (MOS), which are gel droplets that encapsulate tumor cells creating miniature avatars of a patient’s tumor and are amenable to high throughput dispensing and drug studies. In our current study, we evaluated the feasibility of generating and dosing MOS from ABC samples as a novel drug screen platform that led to the development and enrollment of a precision oncology trial, known as MODEL-ABC. Methods: We first performed a proof-of-concept study on 21 samples from patients with ABC and generated MOS. Drug screens were then performed on these MOS across 7 standard of care (SOC) chemotherapies commonly prescribed for ABC. These data resulted in a platform for the MODEL-ABC study that enrolled patients with ABC of any ER, PR, or HER2 subtype who were eligible for single agent chemotherapy to determine the feasibility of using MOS to predict response to therapy. In this study, biopsies from lesions ≥ 2 cm were obtained as part of SOC to generate MOS and perform drug screens. The patient subsequently received single agent chemotherapy per physicians’ choice with either carboplatin, capecitabine, paclitaxel, eribulin, liposomal doxorubicin, gemcitabine, or vinorelbine. MOS were treated using the same chemotherapy as the patient along with 1-3 alternative single agent chemotherapies. Results: We generated MOS from 19/21 samples (90% success rate) across several breast cancer subtypes. Dose response curves of MOS were successfully generated across the 7 chemotherapies with a mean turnaround time of 21±7 days validating the clinical applicability of MOS and leading to the MODEL-ABC trial. As of February 14, 2023, 4 of 15 patients have enrolled onto MODEL-ABC. All biopsies provided sufficient tissue for MOS generation, and drug screens were performed in 14-28 days across 2-4 chemotherapies. Two patients received capecitabine, one patient received eribulin, and one patient received paclitaxel. Full results of MODEL-ABC including correlation between MOS drug response and patient outcomes will be reported at the meeting. Conclusions: Our platform enables efficient establishment of MOS from ABC patient samples and allows for drug dosing studies to be performed in a clinically meaningful timeframe. Our preliminary data suggests it is feasible to obtain biopsies for MOS development and perform drug screens within 14 days. These findings provided the foundation for evaluating this technology as a potential ABC diagnostic tool and warrants further clinical development in ABC. Clinical trial information: NCT04655573 .

Precision Oncology: Evolving Clinical Trials across Tumor Types.

Advances in molecular technologies and targeted therapeutics have accelerated the implementation of precision oncology, resulting in improved clinical outcomes in selected patients. The use of next-generation sequencing and assessments of immune and other biomarkers helps optimize patient treatment selection. In this review, selected precision oncology trials including the IMPACT, SHIVA, IMPACT2, NCI-MPACT, TAPUR, DRUP, and NCI-MATCH studies are summarized, and their challenges and opportunities are discussed. Brief summaries of the new ComboMATCH, MyeloMATCH, and iMATCH studies, which follow the example of NCI-MATCH, are also included. Despite the progress made, precision oncology is inaccessible to many patients with cancer. Some patients' tumors may not respond to these treatments, owing to the complexity of carcinogenesis, the use of ineffective therapies, or unknown mechanisms of tumor resistance to treatment. The implementation of artificial intelligence, machine learning, and bioinformatic analyses of complex multi-omic data may improve the accuracy of tumor characterization, and if used strategically with caution, may accelerate the implementation of precision medicine. Clinical trials in precision oncology continue to evolve, improving outcomes and expediting the identification of curative strategies for patients with cancer. Despite the existing challenges, significant progress has been made in the past twenty years, demonstrating the benefit of precision oncology in many patients with advanced cancer.

Prospective ctDNA genotyping for treatment selection in metastatic castration-resistant prostate cancer (mCRPC): The Canadian Cancer Trials Group phase II PC-BETS umbrella study.

218 Background: Precision oncology trials in mCRPC rely on genomic profiling of tumor tissue but testing failure rates are 30-40%. Incorporating liquid biopsy screening into trial designs may address limitations of tissue-only genotyping. We report findings from the first 500 plasma samples screened on Prostate Cancer Biomarker Enrichment and Treatment Selection (PC-BETS), a phase II multicenter, eight-arm Canadian umbrella trial (NCT03385655) using circulating tumor DNA (ctDNA) to match mCRPC patients to biomarker (BM)-informed targeted therapies. Methods: mCRPC patients previously treated with novel androgen receptor inhibitor therapy were eligible after PSA and/or radiological progression. Plasma cell-free DNA and matched leukocyte DNA underwent deep targeted sequencing with an exon-limited panel (Feb 2017-Sep 2020), or an expanded panel integrating select introns and a genome-wide copy number grid (July 2020-present). A molecular tumor board (MTB) assigned patients to treatment arms based on prespecified BM criteria (BM+), or by randomization if BM negative (BM-). The primary endpoint was clinical benefit rate (PSA50 response; RECIST CR/PR; or SD ≥12 weeks). We report tumor content (ctDNA%), genomic alterations, and BM status for the whole cohort. Results: As of Nov 2021, 503 samples were screened from 444 patients, with 496 passing quality control. 345 samples (70%) had ctDNA ≥1% (ctDNA+), of which the median ctDNA fraction was 20% (IQR 6-44%). 72% of ctDNA+ samples were BM+ (52% of all screened samples). Driver alterations influencing BM status included AR (76%; 59% gain, 24% mutation), PI3K pathway (37%; PTEN 30%, PIK3CA 6%, AKT 3%), and DNA repair defects (26%: mismatch repair 5%, BRCA2 7%, ATM 6%, CDK12 7%, other 6%). The expanded panel detected additional intronic structural variants in baseline ctDNA+ samples ( PTEN 1% vs 25%; BRCA2 0.6% vs 5%; AR 16% vs 37%), and identified whole genome doubling and segmental deletion events. To date, 167 patients have been enrolled to a substudy (83 BM+, 84 BM-). Median time from blood draw to MTB decision improved over time (first vs second half of screening period: 28 vs 17d) with implementation of optimized lab workflows, standardized genomic reports, and hierarchical genomic eligibility assessment. As of Sep 2022, 485 patients have been screened (updated results will be presented). Conclusions: Prospective centralized screening of ctDNA is feasible for guiding precision oncology initiatives. Improvements to assay design, robust availability of targeted therapies and an adaptive approach to biomarker assessment allowed high detection of actionable tumor alterations. Our framework can be used in future trials to stratify patients according to genomic alteration status. Study accrual to PC-BETS is ongoing, with a screening target of 600 patients. Clinical trial information: NCT02905318, NCT03385655 .

A transcriptomics approach to expand therapeutic options and optimize clinical trials in oncology.

The current model of clinical drug development in oncology displays major limitations due to a high attrition rate in patient enrollment in early phase trials and a high failure rate of drugs in phase III studies. Integrating transcriptomics for selection of patients has the potential to achieve enhanced speed and efficacy of precision oncology trials for any targeted therapies or immunotherapies. Relative gene expression level in the metastasis and normal organ-matched tissues from the WINTHER database was used to estimate in silico the potential clinical benefit of specific treatments in a variety of metastatic solid tumors. As example, high mRNA expression in tumor tissue compared to analogous normal tissue of c-MET and its ligand HGF correlated in silico with shorter overall survival (OS; p < 0.0001) and may constitute an independent prognostic marker for outcome of patients with metastatic solid tumors, suggesting a strategy to identify patients most likely to benefit from MET-targeted treatments. The prognostic value of gene expression of several immune therapy targets (PD-L1, CTLA4, TIM3, TIGIT, LAG3, TLR4) was investigated in non-small-cell lung cancers and colorectal cancers (CRCs) and may be useful to optimize the development of their inhibitors, and opening new avenues such as use of anti-TLR4 in treatment of patients with metastatic CRC. This in silico approach is expected to dramatically decrease the attrition of patient enrollment and to simultaneously increase the speed and detection of early signs of efficacy. The model may significantly contribute to lower toxicities. Altogether, our model aims to overcome the limits of current approaches.