AbstractSelecting patients for phase I cancer trials is crucial to ensure a sufficient life expectancy. Frail patients, better suited for palliative care, should not be exposed to new drugs with minimal benefit. Enrolling patients at high risk of early death can jeopardize the study. Our analysis of two large precision medicine studies used tumor fraction from ctDNA to develop a predictive model, demonstrating notable predictive accuracy and aiding in patient selection..

摘要选择患者进行I期癌症试验对于确保足够的预期寿命至关重要。更适合姑息治疗的体弱患者不应接触获益最小的新药。招募早期死亡风险高的患者可能会危及研究。我们对两项大型精密医学研究的分析使用了ctDNA中的肿瘤分数来开发预测模型，证明了显着的预测准确性并有助于患者选择。。

IntroductionPhase I trials primarily delineate the toxicity profile and establish the appropriate dosage levels of novel drugs or combinations in preparation for Phase II/III studies. A significant challenge for Phase I investigators is determining which patients should be offered entry into these trials.

引言I期试验主要描述毒性特征，并确定新药或组合的适当剂量水平，为II/III期研究做好准备。第一阶段研究人员面临的一个重大挑战是确定哪些患者应该进入这些试验。

Typically, patients chosen for Phase I oncology studies possess a life expectancy exceeding three months. However, a concerning 15-20% succumb within 90 days of inclusion1,2,3. At present, there is a glaring absence of objective and consistently reproducible biomarkers for improving patient selection.

通常，选择用于I期肿瘤学研究的患者的预期寿命超过三个月。。目前，明显缺乏客观且一致可重复的生物标志物来改善患者选择。

While the Royal Marsden Hospital (RMH)1 and the Gustave Roussy Immune (GRIM)4 scores, both validated and equipped with three variable metrics (RMH: albumin, lactate, and number of metastatic sites, GRIM: albumin, lactate, and neutrophil/lymphocyte ratio), offer some guidance, the precision is far from ideal.The use of liquid biopsies is progressively becoming a standard in the clinical guidelines for treating advanced-stage cancer patients5,6,7.

虽然皇家马斯登医院（RMH）1和古斯塔夫·鲁西免疫（GRIM）4评分都经过验证并配备了三个可变指标（RMH：白蛋白，乳酸和转移部位数量，GRIM：白蛋白，乳酸和中性粒细胞/淋巴细胞比率），但提供了一些指导，精确度远非理想。液体活检的使用正在逐渐成为治疗晚期癌症患者的临床指南中的标准5,6,7。

Different designs exist for these ctDNA analyses, with some concentrating on specific gene alterations while others conduct a more extensive analysis of the cancer genome6,8. An interesting technological potential of extensively analyzing ctDNA lies in determining the tumor fraction (TF) by assessing the amount of ctDNA released.

。广泛分析ctDNA的一个有趣的技术潜力在于通过评估释放的ctDNA的量来确定肿瘤分数（TF）。

In a study by Stover and colleagues, TF was calculated in metastatic breast cancer patients by evaluating genomic aneuploidy9. The study discovered that a TF of 10% or more independently indicated prognosis when adjusted for clinical and pathological factors, presenting a hazard ratio (HR) of 2.14 with a 95% confidence interval (CI) between 1.4 and 3.8 (P < 0.001).

在Stover及其同事的一项研究中，通过评估基因组非整倍体来计算转移性乳腺癌患者的TF 9。该研究发现，调整临床和病理因素后，TF为10%或更高独立指示预后，风险比（HR）为2.14，95%置信区间（CI）在1.4至3.8之间（P<0.001）。

Other studies in different tumor types h.

不同肿瘤类型的其他研究h。

Tumoral fraction (TF): Assigned a score of 0 for TF < 10% and 64 for TF ≥ 10%, reflecting its strong prognostic impact.

肿瘤分数（TF）：TF<10%为0，TF≥10%为64，反映了其强烈的预后影响。

Albumin levels: Low albumin is scored at 100 due to its high correlation with poor outcomes, while normal levels are scored at 0.

白蛋白水平：低白蛋白评分为100，因为它与不良结果高度相关，而正常水平评分为0。

Metastatic sites: Patients with ≤2 sites are scored at 0, and those with >2 sites are scored at 28, indicating increased risk with more extensive disease.

转移部位：≤2个部位的患者得分为0，>2个部位的患者得分为28，表明疾病更广泛的风险增加。

Neutrophil-to-lymphocyte ratio (NLR): A score of 0 is given for NLR < 6, and 33 for NLR ≥ 6, highlighting its relevance in inflammation-related prognosis.

中性粒细胞与淋巴细胞比率（NLR）：NLR<6分为0分，NLR≥6分为33分，突出了其与炎症相关预后的相关性。

For instance, a patient with high TF (≥10%), low albumin, more than two metastatic sites, and a high NLR would accumulate a score of 225 points, correlating with a three-month survival probability of approximately 25%. Conversely, a patient with all favorable parameters would score 0 points, aligning with a survival probability nearing 90% (Supplementary Fig.

例如，高TF（≥10%），低白蛋白，两个以上转移部位和高NLR的患者将累积225分，与大约25%的三个月生存概率相关。。

3)Supplementary analysesFurther analyses were conducted to compare the predictive performance of the comprehensive model against simpler models integrating TF with established scores such as the GRIm and RMH scores. The results, detailed in Supplementary Tables 4 and 5, reveal that:.

3） 补充分析进行了进一步的分析，以比较综合模型与将TF与GRIm和RMH评分等既定评分相结合的简单模型的预测性能。补充表4和5中详细列出的结果表明：。

The AUROC for the model combining TF and the GRIm score was 73.87 in the BIP cohort and 72.69 in the STING cohort, which are both lower than those achieved by our comprehensive model.

结合TF和GRIm评分的模型的AUROC在BIP队列中为73.87，在STING队列中为72.69，均低于我们的综合模型。

Similarly, the model incorporating TF and the RMH score produced an AUROC of 71.91 for BIP and 66.47 for STING, further substantiating the superiority of the comprehensive model.

同样，结合TF和RMH评分的模型产生的BIP AUROC为71.91，STING为66.47，进一步证实了综合模型的优越性。

Brier scores were also consistently better in the comprehensive model (0.091 for BIP and 0.093 for STING) compared to the simpler models, indicating better overall prediction accuracy.

与更简单的模型相比，综合模型（BIP为0.091，STING为0.093）的Brier得分也一直更好，表明整体预测准确性更好。

Prognostic value of TF is independent of tumor volumeOur findings demonstrate that TF was independently predictive of OS, regardless of the RMS score, which considers the number of metastatic sites. This indicates that TF’s predictive value is not contingent on tumor burden. To verify this hypothesis, we examined the relationship between TF and volume by analyzing the volume of all visible lesions with a diameter greater than 1 mm on baseline CT scans (refer to the “Methods” section).

TF的预后价值与肿瘤体积无关。研究结果表明，无论RMS评分如何，TF都可以独立预测OS，而RMS评分考虑了转移部位的数量。这表明TF的预测价值不取决于肿瘤负荷。为了验证这一假设，我们通过分析基线CT扫描中直径大于1mm的所有可见病变的体积来检查TF与体积之间的关系（请参阅“方法”部分）。

The mean tumor volume was found to be 253 cm³(range 4–2056). The prevalence of high TF did not significantly differ between patients with high versus low tumor burden (44.4% vs. 40.5%, p = 0.65), suggesting that ctDNA shedding in patients with metastatic disease is more influenced by intrinsic tumor biology than by tumor volume (Supplementary Fig.

发现平均肿瘤体积为253cm³（范围4-2056）。高与低肿瘤负荷患者的高TF患病率无显着差异（44.4%比40.5%，p=0.65），表明转移性疾病患者的ctDNA脱落受内在肿瘤生物学的影响大于肿瘤体积（补充图）。

4).DiscussionWe propose here a prognostic tool developed thanks to the analysis of two large independent prospective cohorts, with various advanced solid tumors and sample sizes close to 1000 patients. The variables that are incorporated in the model ctDNA levels, albumin, NLR, and metastatic sites are easy to assess in daily clinical practice.

4） 。讨论我们在这里提出了一种预后工具，该工具是通过分析两个大型独立的前瞻性队列而开发的，其中包括各种晚期实体瘤和接近1000名患者的样本量。纳入模型ctDNA水平，白蛋白，NLR和转移部位的变量在日常临床实践中很容易评估。

We deliberately decided to use all variables as binary to enhance the feasibility of the tool. This however resulted in observed stepwise (rather than perfectly smooth-line) calibration plots, but importantly model calibration as measured using the Brier score remains very favorable.Modern phase 1 trials demonstrate therapeutic benefits for up to 50% of patients, underscoring the positive impact of recent advancements in cancer treatment13,14,15.

我们特意决定将所有变量用作二进制，以增强该工具的可行性。然而，这导致了观察到的逐步（而不是完全平滑的线）校准图，但重要的是，使用Brier评分测量的模型校准仍然非常有利。现代1期临床试验表明，高达50%的患者具有治疗益处，强调了近期癌症治疗进展的积极影响13,14,15。

In high-volume centers, the demand for enrollment in Phase I trials frequently outstrips the available capacity. This necessitates a rigorous selection process am.

在高容量中心，I期试验的入学需求经常超过可用容量。这需要严格的选择过程am。

Data availability

数据可用性

The datasets generated during and/or analyzed during the current study are not publicly available due to the clinical and confidential nature of the material but can be made available from the corresponding author on reasonable request.

由于材料的临床和保密性质，在当前研究期间生成和/或分析的数据集无法公开获得，但可以根据合理的要求从通讯作者处获得。

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Download referencesAcknowledgementsThis research was funded by Fondation Bergonié and Fondation Gustave Roussy. Fondation Bergonié, Fondation Gustave Roussy.Author informationAuthor notesThese authors contributed equally: Arnaud Bayle, Laila Belcaid, Sophie Cousin, Kilian Trin.Authors and AffiliationsDITEP, Gustave Roussy, Villejuif, FranceArnaud Bayle, Jean-Charles Soria & Antoine ItalianoFaculty of Medicine, University of Denmark, Copenhague, DenmarkLaila BelcaidDepartment of Medicine, Institut Bergonié, Bordeaux, FranceSophie Cousin & Antoine ItalianoUniv.

下载参考文献致谢本研究由Bergonié基金会和Gustave Roussy基金会资助。贝戈尼基金会，古斯塔夫·鲁西基金会。作者信息作者注意到，这些作者做出了同样的贡献：阿尔诺·贝勒（Arnaud Bayle），莱拉·贝尔凯（Laila Belcaid），索菲·堂兄（Sophie Cousin），基利安·特里恩（Kilian Trin）。作者和附属机构迪特普（Itep），古斯塔夫·鲁西（Gustave Roussy），维勒吉夫（Villejuif），弗朗西诺·贝勒（Francernaud Bayle），让·查尔斯·索里亚（Jean-Charles Soria）和安托万·意大利人医学院（Antoine ItalianoUniv），丹麦大学（University of Denmark），科。

Bordeaux, Inserm, Bordeaux Population Health Research Center, Epicene team, Bordeaux, FranceKilian Trin, Simone Mathoulin-Pelissier & Carine BelleraINSERM CIC1401, Clinical and Epidemiological Research Unit, Bergonie Institute, Bordeaux, FranceKilian Trin, Simone Mathoulin-Pelissier & Carine BelleraDepartment of Biopathology, Institut Bergonié, Bordeaux, FranceMelissa Alame, Isabelle Soubeyran & Laura BlouinDepartment of Biopathology, Gustave Roussy, Villejuif, FranceEtienne Rouleau, Ludovic Lacroix & Damien VasseurDepartment of Imaging, University Hospital Centre of Bordeaux, Bordeaux, FranceAmandine CrombeUniversity of Bordeaux, Bordeaux, FranceAmandine Crombe, Simone Mathoulin-Pelissier & Antoine ItalianoAuthorsArnaud BayleView author publicationsYou can also search for this author in.

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PubMed Google ScholarContributionsConcept and design: Antoine Italiano. Acquisition, analysis, or interpretation of data: all authors. Drafting of the manuscript: Antoine Italiano. Critical revision of the manuscript for important intellectual content: all authors. Statistical analysis: Carine Bellera, Kilian Trin.

PubMed谷歌学术贡献概念与设计：Antoine Italiano。数据的获取，分析或解释：所有作者。手稿的起草：Antoine Italiano。重要知识内容手稿的批判性修订：所有作者。统计分析：Carine Bellera，Kilian Trin。

Administrative, technical, or material support: Antoine Italiano. Validation: all authors. Supervision: Antoine Italiano.Corresponding authorCorrespondence to.

行政、技术或物质支持：Antoine Italiano。验证：所有作者。监督：Antoine Italiano。对应作者对应。

Antoine Italiano.Ethics declarations

安托万·意大利人。道德宣言

Competing interests

相互竞争的利益

A.I.: research grants and advisory board BAYER, BMS, CHUGAI, DAIICHI-SANKYO, IPSEN, MERCK, MSD, NOVARTIS, PFIZER ROCHE. The other authors declare that they have no competing interests.

A、 一：研究资助和咨询委员会拜耳、BMS、CHUGAI、DAIICHI-SANKYO、IPSEN、默克、MSD、诺华、辉瑞-罗氏。其他作者宣称他们没有相互竞争的利益。

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Reprints and permissionsAbout this articleCite this articleBayle, A., Belcaid, L., Cousin, S. et al. Tumor fraction-based prognostic tool for cancer patients referred to early phase clinical trials.

转载和许可本文引用本文Bayle，A.，Belcaid，L.，Cousin，S。等人。基于肿瘤分数的癌症患者预后工具涉及早期临床试验。

npj Precis. Onc. 8, 227 (2024). https://doi.org/10.1038/s41698-024-00685-9Download citationReceived: 15 March 2024Accepted: 28 August 2024Published: 07 October 2024DOI: https://doi.org/10.1038/s41698-024-00685-9Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

npj精度。。8227（2024）。https://doi.org/10.1038/s41698-024-00685-9Download引文收到日期：2024年3月15日接受日期：2024年8月28日发布日期：2024年10月7日OI：https://doi.org/10.1038/s41698-024-00685-9Share。复制到剪贴板。

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