JCO precision oncology最新文献

Purpose: The Targeted Agent and Profiling Utilization Registry Study is a phase II basket trial evaluating the antitumor activity of targeted agents in patients with advanced cancer and genomic alterations. Results of four cohorts of patients with ATM-altered tumors treated with olaparib are reported: colorectal cancer (CRC), lung cancer (LC), pancreatic cancer (PC), and other solid tumors (histology-pooled, HP).

Methods: Eligible patients had advanced solid tumors, measurable disease (RECIST), 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 histology-specific cohorts, Simon's two-stage design was based on a null DC rate of 15% versus 35% (power = 0.85; α = .10). For the HP cohort, the hypothesized null DC rate of 15% was rejected if the lower limit of a one-sided 90% CI was >15%. Secondary end points were OR, progression-free survival, overall survival, duration of response or SD, and safety.

Results: Patients with CRC (n = 30), LC (n = 20), PC (n = 28), or other advanced cancers (n = 38) with ATM alterations were enrolled. The DC rates were 23% (one-sided 90% CI, 8 to 100; P = .38), 45% (one-sided 90% CI, 32 to 100; P = .0004), 28% (one-sided 90% CI, 14 to 100; P = .14), and 25% (one-sided 90% CI, 16 to 100), respectively. The null hypothesized 15% DC rate was rejected for the LC and HP cohorts but not the CRC and PC cohorts. Twenty of 116 patients (17%) experienced treatment-related grade 3 adverse events (AE) or serious AEs.

Conclusion: Olaparib met the prespecified criteria to declare a signal of activity in patients with ATM-altered cancer within the LC and HP cohorts but not the CRC or PC cohorts.

The presence of circulating tumor DNA (ctDNA) in patients indicates post-treatment molecular residual disease (MRD). Given the complexity of ctDNA-based MRD detection tests, consensus on analytical validation (AV) criteria is needed. To address this, the Blood Profiling Atlas in Cancer (BLOODPAC) Consortium's MRD AV Working Group evaluated existing protocols to develop standardized guidance for tumor-informed assays. Protocols pertaining to blood collection tube types, quantitative output, tissue processing, tumor or matched normal sequencing, software, and clinical validation were considered out of scope. After alignment on objectives and assumptions, study designs on the basis of best practices in the field, available assay validation guidance documents, and unique performance challenges for tumor-informed MRD assays were authored. Each protocol contains introduction, experimental design, statistical analysis, and an example data presentation per the US Food and Drug Administration (FDA) Center for Devices and Radiological Health standard format. Biostatisticians were consulted to define minimal test requirements, sample size, and appropriate statistical analyses. The protocols were submitted to the FDA via the presubmission process for formal written feedback followed by a meeting. BLOODPAC's generic protocols for the AV of tumor-informed ctDNA assays for MRD are designed to provide test developers with a core baseline of standardized AV protocols such that methods described can be adapted and applied for any tumor-informed MRD assay irrespective of technology, panel design algorithm, or workflow component. These protocols aim to optimize test developers' presubmission reviews with the FDA, ensuring productive meetings while enabling reviewers to streamline feedback. As always, test developers are encouraged to communicate with FDA directly around their particular AV methods.

Purpose: RAS mutation is a key biomarker of anti-epidermal growth factor receptor (EGFR) antibody resistance in colorectal cancer (CRC). However, the clinical impact of RAS amplification on the efficacy of anti-EGFR therapy remains unclear. This study aimed to characterize RAS-amplified CRC and evaluate the sensitivity of these tumors to anti-EGFR antibodies.

Methods: We conducted a retrospective observational study using the Center for Cancer Genomics and Advanced Therapeutics database in Japan, which includes clinical and genomic data from patients who underwent comprehensive genomic profiling. We analyzed the data from 9,135 patients with unresectable colorectal adenocarcinoma (CRA) who underwent FoundationOne CDx testing.

Results: RAS amplification was identified in 2.1% (188/9,135) of patients with CRA. Among 1,649 patients with RAS wild-type CRA who received first-line chemotherapy with anti-EGFR antibodies, those with RAS amplification had a lower overall response rate (ORR) and a shorter time to treatment failure (TTF) compared with those without RAS amplification (ORR, 37.5% [21/56] v 52.9% [843/1,593]; median TTF, 195 days [95% CI, 129 to 224] v 274 days [95% CI, 258 to 293]; P = .023 and P = .005, respectively). By contrast, among 4,858 patients treated with bevacizumab, no significant differences were observed in ORR (37.3% [31/83] v 37.1% [1,772/4,775]; P = .964) or TTF (median, 231 days [95% CI, 175 to 273] v 259 days [95% CI, 252 to 270]; P = .445).

Conclusion: RAS amplification is a rare alteration that may confer resistance to anti-EGFR antibodies. Assessing RAS amplification status may help guide the appropriate use of anti-EGFR antibodies in clinical practice.

Purpose: High levels of DNA replication stress and defects in the DNA damage response (DDR) pathways are vulnerabilities of many poor prognosis childhood malignancies. Ataxia telangiectasia and Rad3-related protein (ATR) is a key regulator of these pathways and constitutes an attractive target, especially in combination. However, the malignancies where ATR inhibitors have maximum benefit and synergistic combinations differ between adults and children.

Design: ACCELERATE convened a multistakeholder meeting and conducted review and analysis to propose the optimal pathway for the development of ATR inhibitors in pediatric malignancies.

Results: Considering the lack of identified biomarkers, the initial evaluation of ATR inhibitors should focus on Ewing sarcoma, rhabdomyosarcoma, and neuroblastoma in view of their high levels of DNA replication stress and defects in DDR pathways. Early phase trials of ATR inhibitors should be iterative, based on a clear hypothesis with responders and nonresponders undergoing detailed molecular analysis and a revised new hypothesis generated. Trial designs should restrict monotherapy evaluation to a brief exposure in a small number of patients and progress rapidly to combinations. Highlighted combination partners are poly(ADP-ribose) polymerase inhibitors and antibody drug conjugates with topoisomerase I inhibitor payloads. Combinations with ALK inhibitors (in ALK/MYCN-aberrant neuroblastoma) and aurora A kinase (in MYCN-amplified) are supported by robust mechanisms of action and preclinical data. Early interactions with regulators are crucial, and early phase clinical trials should be conducted in regulatory-approved, academic-sponsored, industry-supported, platform trials.

Conclusion: ATR inhibitors are a prototype for the development of medicinal products in a limited pediatric population. For the substantial potential of ATR inhibitors in children with malignancy to be realized, strategic planning between academia, industry, regulators, and patient advocates is vital.

Purpose: This study evaluates deep learning (DL) approaches, particularly long short-term memory (LSTM) networks, for analyzing dynamic changes in the hepatocellular carcinoma (HCC) tumor microenvironment (TME) to improve disease understanding and treatment prediction.

Materials and methods: Multimodal HCC TME data (high-throughput sequencing, protein expression, and time-series imaging) were integrated. Spatial features were extracted using convolutional neural networks (CNNs), while temporal patterns were modeled with LSTMs. Generative adversarial networks (GANs) augmented data for robust training. Model performance was assessed on the basis of dynamic TME characterization and outcome prediction accuracy.

Results: LSTMs demonstrated superior performance in analyzing TME time-series data, effectively capturing long-term dependencies and predicting cellular interactions (accuracy: 92.3% v 85.7% for CNNs alone). The integrated DL framework successfully characterized spatiotemporal TME evolution and treatment response patterns.

Conclusion: This study demonstrates that LSTMs are powerful tools for analyzing and understanding the dynamic changes in the HCC immune microenvironment. Their strong capability in time-series data analysis provides new opportunities for uncovering the mechanisms of HCC progression and optimizing therapeutic strategies. However, because of the lack of detailed etiological information, stratified validation across different etiologies such as hepatitis B virus, hepatitis C virus, and non-alcoholic steatohepatitis has not yet been conducted in this study and should be addressed in future work. The findings highlight the important role of DL technologies in future research and treatment of HCC.