Matching Patients to Clinical Trials with Large Language Models

Clinical trials are vital in advancing drug development and evidence-based medicine, but their success is often hindered by challenges in patient recruitment. In this work, we investigate the potential of large language models (LLMs) to assist individual patients and referral physicians in identifying suitable clinical trials from an extensive selection. Specifically, we introduce TrialGPT, a novel architecture employing LLMs to predict criterion-level eligibility with detailed explanations, which are then aggregated for ranking and excluding candidate clinical trials based on free-text patient notes. We evaluate TrialGPT on three publicly available cohorts of 184 patients and 18,238 annotated clinical trials. The experimental results demonstrate several key findings: First, TrialGPT achieves high criterion-level prediction accuracy with faithful explanations. Second, the aggregated trial-level TrialGPT scores are highly correlated with expert eligibility annotations. Third, these scores prove effective in ranking clinical trials and exclude ineligible candidates. Our error analysis suggests that current LLMs still make some mistakes due to limited medical knowledge and domain-specific context understanding. Nonetheless, we believe the explanatory capabilities of LLMs are highly valuable. Future research is warranted on how such AI assistants can be integrated into the routine trial matching workflow in real-world settings to improve its efficiency

Applications of large language models in cancer care: current evidence and future perspectives

The development of large language models (LLMs) is a recent success in the field of generative artificial intelligence (AI). They are computer models able to perform a wide range of natural language processing tasks, including content generation, question answering, or language translation. In recent months, a growing number of studies aimed to assess their potential applications in the field of medicine, including cancer care. In this mini review, we described the present published evidence for using LLMs in oncology. All the available studies assessed ChatGPT, an advanced language model developed by OpenAI, alone or compared to other LLMs, such as Google Bard, Chatsonic, and Perplexity. Although ChatGPT could provide adequate information on the screening or the management of specific solid tumors, it also demonstrated a significant error rate and a tendency toward providing obsolete data. Therefore, an accurate, expert-driven verification process remains mandatory to avoid the potential for misinformation and incorrect evidence. Overall, although this new generative AI-based technology has the potential to revolutionize the field of medicine, including that of cancer care, it will be necessary to develop rules to guide the application of these tools to maximize benefits and minimize risks

Loss of vision associated with angioid streaks in beta-thalassemia intermedia

An acquired diffuse elastic tissue defect that resembles inherited pseudoxanthoma elasticum ( PXE) has been noticed with a significant age-related frequency in hemoglobin disorders, especially b-thalassemia and has been held responsible for a number of complications observed in these cases, some of which are quite severe. We report here two patients with b-thalassemia intermedia, who presented with severe visual acuity impairment associated with angioid streaks, the typical ocular manifestation of PXE

Current Diagnosis and Management of Immune Related Adverse Events (irAEs) Induced by Immune Checkpoint Inhibitor Therapy

The indications of immune checkpoint inhibitors (ICIs) are set to rise further with the approval of newer agent like atezolimumab for use in patients with advanced stage urothelial carcinoma. More frequent use of ICIs has improved our understanding of their unique side effects, which are known as immune-related adverse events (irAEs). The spectrum of irAEs has expanded beyond more common manifestations such as dermatological, gastrointestinal and endocrine effects to rarer presentations involving nervous, hematopoietic and urinary systems. There are new safety data accumulating on ICIs in patients with previously diagnosed autoimmune conditions. It is challenging for clinicians to continuously update their working knowledge to diagnose and manage these events successfully. If diagnosed timely, the majority of events are completely reversible, and temporary immunosuppression with glucocorticoids, infliximab or other agents is warranted only in the most severe grade illnesses. The same principles of management will possibly apply as newer anti- cytotoxic T lymphocytes-associated antigen 4 (CTLA-4) and programmed cell death protein 1 (PD-1/PD-L1) antibodies are introduced. The current focus of research is for prophylaxis and for biomarkers to predict the onset of these toxicities. In this review we summarize the irAEs of ICIs and emphasize their growing spectrum and their management algorithms, to update oncology practitioners

Efficacy and safety of PARP inhibitors in metastatic castration-resistant prostate cancer: A systematic review and meta-analysis of clinical trials

Introduction: PARP inhibitors (PARPi) are a standard-of-care (SoC) treatment option for patients with metastatic castration-resistant prostate cancer (mCRPC). several clinical trials have shown the potential of combining PARPi with other anticancer agents. Therefore, we conducted a systematic review and meta-analysis to comprehensively evaluate the efficacy and safety of PARPi in patients with metastatic prostate cancer. methods: MEDLINE, cochrane CENTRAL, EMBASE, CINAHL, and web of science were searched on march 22nd, 2023, for phase 2 or 3 clinical trials. efficacy (progression-free survival [PFS], overall survival [OS], PSA decline >50% [PSA50], and objective response rate [ORR]) and safety outcomes were assessed in the included studies. results: seventeen clinical trials (PARPi monotherapy [n = 7], PARPi + androgen-receptor signaling inhibitors [ARSI] [n = 6], and PARPi + immune checkpoint inhibitors [ICI] [n = 4]) were included in the quantitative analyses. PARPi monotherapy improved radiographic PFS and OS over SoC in mCRPC patients with alterations in BRCA1 or BRCA2 genes but not in those with alterations in the ATM gene. Higher rates of PSA50 and ORR were reported in participants treated with PARPi + ARSI than in single-agent PARPi or PARPi + ICI. although the rate of high-grade adverse events was similar across all groups, treatment discontinuation was higher in patients treated with PARPi-based combinations than PARPi monotherapy. Conclusion: The efficacy of PARPi is not uniform across mCRPC patients with alterations in DNA damage repair genes, and optimal patient selection remains a clinical challenge. no unexpected safety signals for this class of agents emerged from this analysis

Real-world pan-cancer landscape of frameshift mutations (FSM) and their role in predicting responses to immune checkpoint inhibitors (ICI) in patients (pts) with tumors with low tumor mutational burden (TMB)

2599 Background: Pembrolizumab was recently approved in tumors with TMB ≥10 mut/Mb. FSM can complement TMB in predicting ICI responses. We obtained a real-world dataset of genomic alterations from 250,813 samples to examine the distribution of TMB and FSM across a variety of malignancies. We then conducted a multi-institutional retrospective review of pts treated with ICI. Methods: Database samples were sequenced by Foundation Medicine using hybrid capture genomic profiling to evaluate all classes of genomic alterations in at least 315 genes. The clinical cohort included pts with metastatic solid malignancies who received ICI and had undergone commercial next-generation sequencing (NGS). Pts were classified into four distinct groups: TMB-L ( < 10mut/Mb)/ FS-A (absent FSM), TMB-H (≥10mut/Mb)/ FS-A, TMB-L /FS-P (present, ≥1 FSM) and TMB-H/FS-P. Progression-free survival (PFS), overall survival (OS), and response rate (RR) were compared between the groups. Results: 246,252 MSS and 4,561 MSI-High samples were segregated by histology and divided into four distinct groups based on the TMB and FSM. For the MSS cohort the distribution was: TMB-L/FS-A (N = 111,065, 45%), TMB-H/FS-A(N = 15,313, 6%), TMB-L /FS-P (N = 98,389, 40%) and TMB-H/FS-P (N = 21,485, 9%). In the ICI-treated clinical cohort, there were 230 pts in 12 histology groups; 212 had information on TMB and FSM. The most common primary sites were GI (N = 39), melanoma (N = 37), GU (N = 32) and H&N cancer (N = 21). 159 pts received single ICI and 53 dual ICI. 196 tumors were MSS, 11 MSI, and 5 unknown. Group distribution: TMB-L/FS-A 80 pts (38%), TMB-L/FS-P 57pts (27%), TMB-H/FS-A 36pts (17%), TMB-H/FS-P 39pts (18%). FS-P was associated with higher RR 23.81 vs. 12.8 % (p = 0.02). Regardless of TMB, the median PFS for FS-P vs. FS-A was 7.9 and 4.0 mo, respectively (p < 0.01). TMB-L/FS-P had superior PFS (5.1 mo) compared to TMB-L/FS-A (3.6 mo) group (p < 0.01). The 15-month PFS probability was 12% for TMB-L/FS-A vs. 38% for TMB-L/FS-P. No statistically significant difference was detected in OS between the groups. From the pan-cancer cohort, histologies with more than 40% of samples in the TBM-L/FS-P (MSS) group were: CRC, RCC, PDAC, biliary, breast, esophageal, and endometrial cancers. Additional genomic data will be presented. Conclusions: FSM are frequently found on commercial NGS testing in tumors that are MSS and TMB-L. The presence of FSM may complement TMB in predicting benefit from immunotherapy. If validated in additional cohorts, FSM presence could be utilized to identify pts that may benefit from ICI, particularly for tumors with low TMB

Real-world pan-cancer landscape of frameshift mutations and their role in predicting responses to immune checkpoint inhibitors in cancers with low tumor mutational burden

Background Pembrolizumab is FDA approved for tumors with tumor mutational burden (TMB) of ≥10 mutations/megabase (mut/Mb). However, the response to immune checkpoint inhibitors (ICI) varies significantly among cancer histologies. We describe the landscape of frameshift mutations (FSs) and evaluated their role as a predictive biomarker to ICI in a clinical cohort of patients.Methods Comprehensive genomic profiling was performed on a cohort of solid tumor samples examining at least 324 genes. The clinical cohort included patients with metastatic solid malignancies who received ICI monotherapy and had tumor sequencing. Progression-free survival (PFS), overall survival, and objective response rates (ORR) were compared between the groups.Results We analyzed 246,252 microsatellite stable (MSS) and 4561 samples with microsatellite instability across solid tumors. Histologies were divided into groups according to TMB and FS. MSS distribution: TMB-L (&lt;10 mut/Mb)/FS-A (absent FS) (N=111,065, 45%), TMB-H (≥10 mut/Mb)/FS-A (N=15,313, 6%), TMB-L/FS-P (present ≥1 FS) (N=98,389, 40%) and TMB-H/FS-P (N=21,485, 9%). FSs were predominantly identified in the p53 pathway. In the clinical cohort, 212 patients were included. Groups: TMB-L/FS-A (N=80, 38%), TMB-H/FS-A (N=36, 17%), TMB-L/FS-P (N=57, 27%), TMB-H/FS-P (N=39, 18%). FSs were associated with a higher ORR to ICI, 23.8% vs 12.8% (p=0.02). TMB-L/FS-P had superior median PFS (5.1 months) vs TMB-L/FS-A (3.6 months, p&lt;0.01). The 12-month PFS probability was 34% for TMB-L/FS-P vs 17.1% for TMB-L/FS-A.Conclusions FSs are found in 47% of patients with MSS/TMB-L solid tumors in a pan-cancer cohort. FS may complement TMB in predicting immunotherapy responses, particularly for tumors with low TMB