JCO precision oncology最新文献

Purpose: Cisplatin-based combination chemotherapy (CHT) is standard of care in metastatic urothelial cancer (mUC); however, no predictive molecular biomarkers are available for clinical use. The aim of this study was to investigate the impact of molecular subtypes in relation to treatment response and survival in patients with mUC treated with first-line CHT.

Patients and methods: Molecular subtype classification according to the Lund Taxonomy (LundTax) was performed by tumor transcriptomic profiling and immunostaining in a retrospective cohort. Molecular subtypes were investigated in relation to the primary end point overall response rate (ORR) and secondary end points progression-free survival (PFS) and overall survival (OS). Differential gene expression and association to treatment response were explored.

Results: Ninety-five patients with mUC were classified into urothelial-like (Uro, 43%), genomically unstable (GU, 26%), basal squamous-like (Ba/Sq, 20%), mesenchymal-like (Mes-like, 8%), and small cell neuroendocrine-like (Sc/NE, 3%) subtypes. Patients with Mes-like tumors had lower ORR (14%) compared with Uro (70%), GU (77%), Ba/Sq (75%), and Sc/NE (67%; odds ratio, 0.06 [95% CI, 0.01 to 0.54], P = .012). Furthermore, patients with Mes-like tumors had significantly shorter PFS (hazard ratio [HR], 5.18 [95% CI, 2.28 to 11.76], P < .001) and OS (HR, 3.19 [95% CI, 1.45 to 7.03], P = .004). Patients with Uro and GU showed the longest survival. In responders, an enrichment of downregulated stromal- and immune-related genes was seen. Downregulation of interferon-induced transmembrane protein 2 was associated with increased ORR and improved OS.

Conclusion: This study identifies different CHT responses by LundTax molecular subtypes in patients with mUC, where the Mes-like subtype was associated with lower response rate and shorter survival.

Purpose: The clinical and research FPG500 program (ClinicalTrials.gov identifier: NCT06020625) is currently ongoing at the Fondazione Policlinico Universitario Agostino Gemelli IRCCS to tailor matched targeted therapies (MTTs) according to biomarkers predictive of response identified by comprehensive genome profiling (CGP).

Materials and methods: The non-small cell lung cancer (NSCLC) cohort results from the FPG500 program are outlined. CGP was performed by TruSight Oncology 500 High Throughput (TSO500HT) assay or Oncomine Focus Assay plus Archer's FusionPlex Lung Panel according to tumor cell content and DNA/RNA quantity. Relevant issues for Molecular Tumor Board (MTB) evaluation included uncommon genomic findings, evaluation for off-label therapies, uncertain result confirmation, and variants of suspect germline origin requiring genetic counseling. Progression-free survival (PFS) and overall survival (OS) for the enrolled patients were assessed using Kaplan-Meier analysis.

Results: In 2022, 283 patients with NSCLC were considered for sequencing, with 93% meeting eligibility criteria. TSO500HT sequencing was conducted in 76% of patients. Follow-up data were obtained for 187 patients, among whom 81% received treatment. Potential driver alterations were identified in 59% of patients, with 41% receiving MTT: 25% were prescribed approved MTTs, whereas 16% gained access to experimental drugs post-MTB evaluation; of note, 18% did not receive any MTT because the regimen was not yet reimbursed in our country. Median PFS and OS varied among treatment groups, with standard chemotherapy/immunotherapy at 7.7 and 10.7 months, approved tyrosine kinase inhibitors at 18.8 and 23.9 months, and MTT post-MTB discussion at 14 and 23.4 months, respectively.

Conclusion: The early data of the FPG program (NSCLC cohort) support the implementation of CGP and MTB in clinical practice to grant access to patients harboring actionable molecular alterations to the most effective and individualized available treatment options, thus improving their survival outcomes.

Case series describing excellent outcomes for patients with dMMR GI cancer after resection of a single progressive lesion under immunotherapy.

Polyclonal convergent evolution to PARPi resistance in a patient with metastatic breast cancer with gPALB2.

Purpose: Robotic-assisted proctectomy (RAP) has emerged as the predominant surgical approach for patients with rectal cancer in recent years; although good postoperative patient recovery with accurate prediction is a guarantee of adaptive surveillance management, there is still a lack of easy-to-use prognostic tools and risk scores designed specifically for those patients undergoing RAP.

Methods: This study used the electronic health records of 506 RAP participants, including a National Specialist Center for da Vinci Robotic Colorectal Surgery (NSCVRCS) meta cohort, and an independent external validation Sun Yat-sen Memorial Hospital cohort. In the NSCVRCS meta cohort, patients were divided into a discovery cohort (70%, n = 268), where the best-fit model was applied to model our prediction system, RAP-AIscore. Subsequently, an internal validation process for RAP-AIscore was conducted using a replication cohort (30%, n = 116). The study designed and implemented a large-scale artificial intelligence (AI) hybrid framework to identify the best strategy for building a survival assessment system, the RAP-AIscore, from 132 potential modeling scenarios through a combination of iterative cross-validation, Monte Carlo cross-validation, and bootstrap resampling. The 10 variables most relevant to clinical interpretability were identified on the basis of the AI hybrid optimal model values, which helps provide reliable prognostic survival guidance for new patients.

Results: The consistent evaluation of discrimination, calibration, generalization, and prognostic value across cohorts reaffirmed the accuracy and robust extrapolation capability of this system. The 10 feature variables most associated with clinical interpretability on the basis of Shapley values were identified, facilitating reliable prognostic survival guidance for new patients.

Conclusion: This study introduces a promising and informative tool, the RAP-AIscore, which can be explained through nomograms for interpreting clinical outcomes. It facilitates postoperative risk stratification management and enhances clinical management of prognosis for RAP patients.

Purpose: Rapidly expanding medical literature challenges oncologists seeking targeted cancer therapies. General-purpose large language models (LLMs) lack domain-specific knowledge, limiting their clinical utility. This study introduces the LLM system Medical Evidence Retrieval and Data Integration for Tailored Healthcare (MEREDITH), designed to support treatment recommendations in precision oncology. Built on Google's Gemini Pro LLM, MEREDITH uses retrieval-augmented generation and chain of thought.

Methods: We evaluated MEREDITH on 10 publicly available fictional oncology cases with iterative feedback from a molecular tumor board (MTB) at a major German cancer center. Initially limited to PubMed-indexed literature (draft system), MEREDITH was enhanced to incorporate clinical studies on drug response within the specific tumor type, trial databases, drug approval status, and oncologic guidelines. The MTB provided a benchmark with manually curated treatment recommendations and assessed the clinical relevance of LLM-generated options (qualitative assessment). We measured semantic cosine similarity between LLM suggestions and clinician responses (quantitative assessment).

Results: MEREDITH identified a broader range of treatment options (median 4) compared with MTB experts (median 2). These options included therapies on the basis of preclinical data and combination treatments, expanding the treatment possibilities for consideration by the MTB. This broader approach was achieved by incorporating a curated medical data set that contextualized molecular targetability. Mirroring the approach MTB experts use to evaluate MTB cases improved the LLM's ability to generate relevant suggestions. This is supported by high concordance between LLM suggestions and expert recommendations (94.7% for the enhanced system) and a significant increase in semantic similarity from the draft to the enhanced system (from 0.71 to 0.76, P = .01).

Conclusion: Expert feedback and domain-specific data augment LLM performance. Future research should investigate responsible LLM integration into real-world clinical workflows.

Imatinib may be a useful targeted agent for patients with advanced gastric adenocarcinoma who have KIT mutations.

Purpose: Cancer of unknown primary (CUP) is a syndrome comprising metastatic cancers without a clinically identified primary site. Although patients with CUP have an unfavorable prognosis, treatment with site-specific therapies guided by clinical features, standard pathology, and molecular assays can improve overall survival. The 92-gene assay (CancerTYPE ID) is a gene expression-based classifier that helps identify the tissue of origin for metastatic cancers with unknown or uncertain diagnoses. This study reports the frequency of selected molecular aberrations of oncogenes, including KRAS, IDH1/2, BRCA1/2, and BRAF, in patients with CUP in the MOSAIC database to highlight potential treatment options.

Methods: MOSAIC is a database of patients with CUP submitted for CancerTYPE ID testing and NeoTYPE biomarker testing. Tumor biopsy samples were analyzed by CancerTYPE ID for tumor type identification and further tested for molecular aberrations of oncogenes, including KRAS, IDH1/2, BRCA1/2, and BRAF.

Results: CancerTYPE ID identified a specific tumor type in 92.5% (2,929 of 3,168) of CUP cases in the MOSAIC database. The most commonly identified histological type was adenocarcinoma (75.4%), with pancreaticobiliary being the most common molecularly diagnosed cancer (24.9%). Aberrations in KRAS, IDH1/2, BRCA, and BRAF genes were identified in 18.8% (n = 597) of biopsies. A cancer-specific US Food and Drug Administration (FDA)-approved or investigational targeted therapy was potentially available for 24.6% (n = 147) of these patients.

Conclusion: This retrospective analysis supports incorporating CancerTYPE ID into the evaluation for patients with CUP to help determine the tissue of origin and identify actionable genetic alterations. This approach may allow more patients with CUP to benefit from site-specific FDA-approved targeted therapies or enrollment into clinical trials.