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Toxicity Adaptive Lists Design: A Practical Design for Phase I Drug Combination Trials in Oncology.

We introduce a novel algorithmic approach to design phase I trials for oncology drug combinations. Our proposed Toxicity Adaptive Lists Design (TALE) is straightforward to implement, requiring the prespecification of a small number of parameters that define rules governing dose escalation, de-escalation, or reassessment of previously explored dose levels. These rules effectively regulate dose exploration and control the number of toxicities. A key feature of TALE is the possibility of simultaneous assignment of multiple-dose combinations that are deemed safe by previously accrued data. A numerical study shows that TALE shares comparable operative characteristics, in terms of identification of the maximum tolerated dose (MTD), to alternative approaches such as the Bayesian optimal interval design, the COPULA, the product of independent beta probabilities escalation, and the continual reassessment method for partial ordering designs while reducing the risk of overdosing patients. The proposed TALE design provides a favorable balance between maintaining patient safety and accurately identifying the MTD. To facilitate the use of TALE, we provide a user-friendly R Shiny application and an R package for computing relevant operating characteristics, such as the risk of assigning highly toxic dose combinations.

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Pharmacodynamic Activity of [18F]-Fluorthanatrace Poly(ADP-ribose) Polymerase Positron Emission Tomography in Patients With BRCA1/2-Mutated Breast Cancer Receiving Talazoparib.

We tested the ability of [18F] fluorthanatrace (FTT), a radiolabeled analog of poly(ADP-ribose) polymerase (PARP)-1 inhibitors, to demonstrate target engagement on positron emission tomography (PET) scans from patients with newly diagnosed primary breast cancer receiving the PARP inhibitor (PARPi) talazoparib. Seven patients with germline BRCA1/2 pathogenic variants underwent [18F]FTT PET-computed tomography scanning at baseline, and five underwent repeat scanning 14 days after talazoparib initiation. Maximum uptake on PET was quantified in the primary tumor, involved nodes, contralateral pectoralis muscle, and lumbar vertebra body level 3, and compared between the two time points. Blocking of [18F]FTT was observed on the second scan. Potentially strong but nonsignificant correlations were found between changes in tumor volume (on ultrasound at 1 month v baseline) and percentage changes in tumor-to-muscle uptake ratio at 14 days from baseline (Spearman rank correlation coefficient r = 1; P = .083); and between the highest-grade hematologic toxicity and baseline bone marrow-to-muscle (B/M) uptake ratio (r = 0.72; P = .068) and percentage change in B/M ratio at 14 days from baseline (r = 0.87; P = .058). We conclude that [18F]FTT can image target engagement by PARPi, but larger studies are needed to determine whether [18F]FTT uptake can predict response to PARPi and whether uptake of [18F]FTT in bone marrow may be an early predictor of hematologic toxicity.

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Palbociclib in Patients With Head and Neck Cancer and Other Tumors With CDKN2A Alterations: Results From the Targeted Agent and Profiling Utilization Registry Study.

Targeted Agent and Profiling Utilization Registry is a phase II basket trial evaluating the antitumor activity of commercially available targeted agents in patients with advanced cancer and targetable genomic alterations. Two cohorts of patients with cyclin-dependent kinase inhibitor 2A (CDKN2A)-mutated tumors treated with palbociclib are reported: one with head and neck cancer (HNC) with both squamous and nonsquamous cell histologies, and one with histology-pooled (HP) cancers. Eligible patients had measurable disease, Eastern Cooperative Oncology Group performance status 0-2, adequate organ function, and no standard treatment options. The primary end point was disease control (DC), defined as objective response (OR) or stable disease (SD) of at least 16+ weeks duration. For the HNC cohort, Simon's two-stage design with a null DC rate of 15% versus 35% (power = 0.85; α = .10) was used. For the HP cohort, the null hypothesis of a DC rate of 15% was rejected if the lower limit of a one-sided 90% CI was >15%. Secondary end points included OR, safety, progression-free survival, overall survival, duration of response, and duration of SD. Seventy patients with HNC (N = 28) or HP cancers (N = 42) were treated with palbociclib. For the HNC cohort, DC and OR rates were 40% (one-sided 90% CI, 27 to 100) and 4% (95% CI, <1 to 18), respectively. The null hypothesis was rejected (P = .002). For the HP cohort, DC and OR rates were 13% (one-sided 90% CI, 6 to 100) and 5% (95% CI, <1 to 17), respectively. The null hypothesis was not rejected. Thirty-one of 70 patients experienced treatment-related grade 3 to 4 adverse events (AEs) or serious AEs, the most common including neutropenia, thrombocytopenia, and leukopenia. Palbociclib met prespecified criteria to declare a signal of activity in patients with HNC with CDKN2A alterations, but not in the HP cohort.

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Antibody-Drug Conjugates in Breast Cancer: Toward a Molecular Perspective Into Clinical Practice.

Antibody-drug conjugates (ADCs) are at the forefront of cancer therapy, combining targeted precision with potent cytotoxicity. Conceived by Paul Ehrlich in the early 1900s, the concept of a magic bullet selectively eliminating cancer cells has evolved alongside bioengineering and cancer biology advancements. ADCs consist of a monoclonal antibody, linker, and cytotoxic payload, designed to target specific antigens on tumor cells while minimizing collateral damage. Mechanistically, ADCs are internalized via endocytosis, releasing the cytotoxic payload within the lysosome, potentially affecting neighboring tumor cells. ADC development has progressed through multiple generations, each addressing limitations of its predecessors. From gemtuzumab ozogamicin to trastuzumab emtansine (T-DM1), and now to third-generation agents such as trastuzumab deruxtecan (DS-8201) and disitamab vedotin (RC48), improvements have been made in target selectivity, potency, linker stability, and reduced off-target effects. Significant success has been seen in ADCs targeting human epidermal growth factor receptor 2 and trophoblast cell-surface antigen 2 antigens, especially in patients with breast cancer, including those resistant to previous therapies. The future of ADCs includes exploring new surface antigens, bispecific antibodies, immune-activating antibodies, radiopharmaceutical-loaded ADCs, and masked ADCs for tissue-specific activation. Ongoing research aims to optimize treatment efficacy while minimizing toxicity, expanding the potential of combination therapy. ADCs represent a promising frontier in precision cancer treatment, with continued research enhancing their potential in breast cancer and beyond. This review provides a comprehensive exploration of ADCs' evolution in breast cancer therapy, offering a molecular perspective to inform clinical practice and update colleagues on this dynamic field.

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Comprehensive Genomic Assessment of Advanced-Stage GI Stromal Tumors Using the Japanese National Center for Cancer Genomics and Advanced Therapeutics Database.

Clinical utility of comprehensive genomic profiling (CGP) for precision medicine has become evident. Although there are several reports on the genomic landscape of GI stromal tumors (GISTs), large-scale data specific to GIST are limited, especially in Asia. Additionally, the applicability of molecular-targeted agents identified using CGP has not been extensively examined. We investigated the status of genomic alterations in Japanese patients with advanced GISTs using the National Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database to identify novel treatment strategies and drug development. We retrospectively reviewed the clinical and CGP data of patients with advanced-stage GIST registered in the C-CAT database to assess the genomic landscape and potential actionable alterations. Data from 144 patients were reviewed. Oncogenic alterations were detected frequently in KIT (78%), CDKN2A (37%), CDKN2B (29%), RB1 (11%), STK11 (10%), TP53 (9%), PDGFRA (6%), and SDHB (6%). Loss of CDKN2A/CDKN2B was only observed in KIT/PDGFRA-mutated GISTs, while alterations in SDHA/SDHB were only detected in KIT/PDGFRA wild-type GISTs. Among 119 KIT/PDGFRA-mutated GISTs, 95 (80%) had oncogenic genomic alterations and 29 (24%) had actionable alterations, excluding KIT and PDGFRA. However, among 25 KIT/PDGFRA wild-type GISTs, 22 (88%) had oncogenic alterations and 11 (44%) had actionable alterations. Representative candidate drugs for genome-matched therapies in KIT/PDGFRA-mutated and wild-type GISTs were as follows: pembrolizumab for tumor mutation burden-high in one and two patients, respectively; poly-adenosine diphosphate ribose polymerase inhibitors for alterations related to homologous recombination deficiency in 12 and one patient, respectively; NTRK inhibitor for ETV6-NTRK3 fusion in one with KIT/PDGFRA wild-type GIST; and human epidermal growth factor receptor 2-antibody-drug conjugate in one with KIT/PDGFRA-mutated GIST. This study highlights the genomic landscape of advanced GISTs and the important role of CGP in identifying rational molecular-targeted therapeutic options.

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Expert-Guided Large Language Models for Clinical Decision Support in Precision Oncology.

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

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