QUANTITATIVE IMAGING BIOMARKERS OF TREATMENT RESPONSE AND PROGNOSIS IN BREAST CANCER

乳腺癌治疗反应和预后的定量成像生物标志物

• 批准号: 10454417
• 负责人: Jia Wu
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10454417
• 关键词: Adjuvant; Adjuvant Therapy; Biological; Biological Assay; Biological Markers; Biopsy; Breast Cancer Patient; Characteristics; Clinical; Complement; Data; Descriptor; Diagnosis; Disease; ERBB2 gene; Ensure; Environment; Eosine Yellowish; Foundations; Future; Genetic Heterogeneity; Genomic approach; Genomics; Heterogeneity; Histologic; Histology; Image; Individual; Kinetics; Lead; MRI Scans; Magnetic Resonance Imaging; Malignant Neoplasms; Manuals; Measures; Medical Imaging; Methods; Modeling; Molecular; Molecular Profiling; Morbidity - disease rate; Morphology; Multiomic Data; Neoadjuvant Therapy; Normal tissue morphology; Oncology; Pathologic; Pathology; Patients; Pattern; Phenotype; Prognosis; Prospective cohort; Proteomics; Protocols documentation; Recurrence; Regimen; Reproducibility; Research; Risk; Role; Semantics; Site; Slide; Stains; Subgroup; Surrogate Markers; TNM; Testing; Texture; The Cancer Genome Atlas; Therapeutic; Toxic effect; Tumor Biology; Validation; Variant; Woman; Work; artificial intelligence algorithm; automated segmentation; base; cancer subtypes; chemotherapy; clinical translation; clinically relevant; cohort; computerized tools; contrast enhanced; effective therapy; feature extraction; genetic predictors; genomic predictors; high risk; image guided; imaging biomarker; imaging study; individualized medicine; innovation; magnetic resonance imaging biomarker; malignant breast neoplasm; molecular marker; molecular pathology; mortality; multimodality; multiple omics; neglect; oncotype; outcome prediction; overtreatment; patient stratification; personalized management; precision medicine; predict clinical outcome; primary endpoint; prognostic; quantitative imaging; radiologist; response; side effect; success; survival prediction; synergism; targeted treatment; tool; transcriptomics; treatment response; tumor; tumor heterogeneity

ABSTRACT Breast cancer is a heterogeneous disease. Around 20% to 30% of women diagnosed with invasive breast cancer will have a recurrence and may eventually die of their disease. Currently, there are no reliable methods to identify which cancers will recur on an individual basis. Because of this, adjuvant therapies are given to nearly all patients with breast cancer, but benefit only a small proportion. A similar dilemma exists for neoadjuvant treatment, many patients fail to pathologically response to chemotherapy, and yet suffer from the associated toxicity. The conventional one-size-fits-all approach causes overtreatment, leading to morbidities and mortalities. To avoid these side effects, biomarkers that stratify patients with clinical relevance are critically needed for precision medicine in breast cancer. Molecular profiling is currently used to stratify breast cancer, but is limited by the requirement for invasive biopsy and confounded by intra-tumor genetic heterogeneity. Conversely, imaging provides a unique opportunity for the noninvasive interrogation of the tumor, its microenvironment, and invasion to surrounding normal tissues. We hypothesize that imaging characteristics reflect underlying tumor biology, and quantitative imaging features can provide independent valuable information, which are synergistic to known clinical, histologic, and genetic predictors. Accordingly, we have planned three specific aims to develop new quantitative imaging biomarkers for breast cancer, as well as clinically and biologically validate them. In Aim 1 we plan to develop automated computational tools to robustly quantify whole tumor, intratumor subregions, and parenchyma phenotypes from multimodal MRI. The curated breast cancer cohort (n=504) from our preliminary study will be analyzed, with available MRI scans and manually-delineated contours of tumor and parenchyma by board-certified radiologists. In Aim 2 we will build imaging feature-based models to predict recurrence-free survival and treatment response separately. By integrating with clinicopathologic and genomic predictors, the comprehensive models can predict clinical outcomes more accurately. The internal cohort (n=450) will be used for discovery, and the multi-center prospective cohort from I-SPY (n=186) will be used for validation. In Aim 3 we will elucidate the biological underpinnings behind our newly identified prognostic and predictive imaging biomarkers, by correlating them with biospecimen-derived phenotypes from the same tumor. In particular, we will investigate multi-omics molecular data as well as tumor morphology from H&E stained pathology slides. Three cohorts will be analyzed, including our internal cohort (n=450), the I-SPY cohort (n=186), and the TCGA cohort (n=1095). For three proposed aims, we have carried preliminary studies to prove the feasibility. By leveraging the richness of available well-annotated data and advanced artificial intelligence algorithms, it will increase the likelihood of success. Our proposed research will point new biomarkers of high value to better predict recurrence and treatment response at the individual level, and lead to better treatment decisions for women with breast cancer.

摘要 乳腺癌是一种异质性疾病。大约20%至30%的女性被诊断患有浸润性乳腺癌， 会复发并最终死于疾病目前，还没有可靠的方法来识别 哪些癌症会根据个体情况复发。正因为如此，几乎所有患者都接受了辅助治疗 乳腺癌，但只有一小部分受益。新辅助治疗也存在类似的困境，许多 患者对化疗没有病理反应，但仍遭受相关毒性。的 传统的“一刀切”做法造成过度治疗，导致发病率和死亡率。避免 对于这些副作用，对具有临床相关性的患者进行分层的生物标志物对于精确性来说是至关重要的。 乳腺癌的治疗分子谱分析目前用于对乳腺癌进行分层，但受到乳腺癌的局限性。 需要进行侵入性活检，并受到肿瘤内遗传异质性的混淆。相反，成像 为肿瘤、其微环境和侵袭的非侵入性检查提供了独特的机会 周围的正常组织。我们假设影像学特征反映了潜在的肿瘤生物学， 定量成像特征可以提供独立的有价值的信息，这与已知的成像特征是协同的。 临床、组织学和遗传学预测因子。因此，我们计划了三个具体目标，以开发新的 乳腺癌的定量成像生物标志物，以及临床和生物学验证它们。目标1 我们计划开发自动化计算工具，以稳健地量化整个肿瘤、肿瘤内亚区域， 来自多模态MRI的实质表型。来自我们初步研究的乳腺癌队列（n=504） 将使用可用的MRI扫描和手动描绘的肿瘤和实质轮廓分析研究 由认证的放射科医生进行在目标2中，我们将建立基于成像特征的模型来预测无复发 存活率和治疗反应。通过整合临床病理学和基因组预测因子， 综合模型可以更准确地预测临床结果。将使用内部队列（n=450） 将使用I-SPY的多中心前瞻性队列（n=186）进行验证。目标3 我们将阐明我们新发现的预后和预测成像背后的生物学基础， 生物标志物，通过将它们与来自相同肿瘤的生物标志物衍生的表型相关联。我们尤其 将从H&E染色的病理切片中研究多组学分子数据以及肿瘤形态。 将分析三个队列，包括我们的内部队列（n=450），I-SPY队列（n=186）和TCGA队列。 队列（n=1095）。对于三个目标，我们进行了初步的研究，以证明其可行性。通过 利用丰富的注释数据和先进的人工智能算法，它将 增加成功的可能性。我们提出的研究将指出新的高价值生物标志物，以更好地 在个体水平上预测复发和治疗反应，并导致更好的治疗决策， 患乳腺癌的女性

项目成果

Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer.

• DOI: 10.1186/s13058-018-1039-2
• 发表时间: 2018-09-03
• 期刊: Breast cancer research : BCR
• 作者: Wu J;Li X;Teng X;Rubin DL;Napel S;Daniel BL;Li R
• 通讯作者: Li R

SwarmDeepSurv: swarm intelligence advances deep survival network for prognostic radiomics signatures in four solid cancers.

• DOI: 10.1016/j.patter.2023.100777
• 发表时间: 2023-08-11
• 期刊: PATTERNS
• 影响因子: 6.5
• 作者: Al-Tashi, Qasem;Saad, Maliazurina B.;Sheshadri, Ajay;Wu, Carol C.;Chang, Joe Y.;Al-Lazikani, Bissan;Gibbons, Christopher;Vokes, Natalie I.;Zhang, Jianjun;Lee, J. Jack;Heymach, John, V;Jaffray, David;Mirjalili, Seyedali;Wu, Jia
• 通讯作者: Wu, Jia

Artificial Intelligence in Digital Pathology to Advance Cancer Immunotherapy.

• 发表时间: 2022
• 期刊: 21st century pathology
• 作者: Chen P;Zhang J;Wu J
• 通讯作者: Wu J

Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in chronic lymphocytic leukemia.

• DOI: 10.1002/path.5795
• 发表时间: 2022-01
• 期刊: The Journal of pathology

Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review.

• DOI: 10.3390/ijms24097781
• 发表时间: 2023-04-24
• 期刊: INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
• 影响因子: 5.6
• 作者: Al-Tashi, Qasem;Saad, Maliazurina B.;Muneer, Amgad;Qureshi, Rizwan;Mirjalili, Seyedali;Sheshadri, Ajay;Le, Xiuning;Vokes, Natalie I.;Zhang, Jianjun;Wu, Jia
• 通讯作者: Wu, Jia