A Nanoradiomics Approach for Differentiation of Tumors Based on Tumor-Associated Macrophage Burden

Intratumoral nanofluidic system enhanced tumor biodistribution of PD-L1 antibody in triple-negative breast cancer

Immune checkpoint inhibitors (ICI), pembrolizumab and atezolizumab, were recently approved for treatment-refractory triple-negative breast cancer (TNBC), where those with Programmed death-ligand 1 (PD-L1) positive early-stage disease had improved responses. ICIs are administered systemically in the clinic, however, reaching effective therapeutic dosing is challenging due to severe off-tumor toxicities. As such, intratumoral (IT) injection is increasingly investigated as an alternative delivery approach. However, repeated administration, which sometimes is invasive, is required due to rapid drug clearance from the tumor caused by increased interstitial fluid pressure. To minimize off-target drug biodistribution, we developed the nanofluidic drug-eluting seed (NDES) platform for sustained intratumoral release of therapeutic via molecular diffusion. Here we compared drug biodistribution between the NDES, intraperitoneal (IP) and intratumoral (IT) injection using fluorescently labeled PD-L1 monoclonal antibody (αPD-L1). We used two syngeneic TNBC murine models, EMT6 and 4T1, that differ in PD-L1 expression, immunogenicity, and transport phenotype. We investigated on-target (tumor) and off-target distribution using different treatment approaches. As radiotherapy is increasingly used in combination with immunotherapy, we sought to investigate its effect on αPD-L1 tumor accumulation and systemic distribution. The NDES-treated cohort displayed sustained levels of αPD-L1 in the tumor over the study period of 14 days with significantly lower off-target organ distribution, compared to the IP or IT injection. However, we observed differences in the biodistribution of αPD-L1 across tumor models and with radiation pretreatment. Thus, we sought to extensively characterize the tumor properties via histological analysis, diffusion evaluation and nanoparticles contrast-enhanced CT. Overall, we demonstrate that ICI delivery via NDES is an effective method for sustained on-target tumor delivery across tumor models and combination treatments.

Targeting Tumor-Associated Macrophages for Imaging

As an important component of the tumor immune microenvironment (TIME), tumor-associated macrophages (TAMs) occupy a significant niche in tumor margin aggregation and respond to changes in the TIME. Thus, targeting TAMs is important for tumor monitoring, surgical guidance and efficacy evaluation. Continuously developing nanoprobes and imaging agents paves the way toward targeting TAMs for precise imaging and diagnosis. This review summarizes the commonly used nanomaterials for TAM targeting imaging probes, including metal-based nanoprobes (iron, manganese, gold, silver), fluorine-19-based nanoprobes, radiolabeled agents, near-infrared fluorescence dyes and ultrasonic nanobubbles. Additionally, the prospects and challenges of designing nanomaterials for imaging and diagnosis (targeting efficiency, pharmacokinetics, and surgery guidance) are described in this review. Notwithstanding, TAM-targeting nanoplatforms provide great potential for imaging, diagnosis and therapy with a greater possibility of clinical transformation.

Applications of machine learning in tumor-associated macrophages

Evaluation of tumor-host interaction and intratumoral heterogeneity in the tumor microenvironment (TME) is gaining increasing attention in modern cancer therapies because it can reveal unique information about the tumor status. As tumor-associated macrophages (TAMs) are the major immune cells infiltrating in TME, a better understanding of TAMs could help us further elucidate the cellular and molecular mechanisms responsible for cancer development. However, the high-dimensional and heterogeneous data in biology limit the extensive integrative analysis of cancer research. Machine learning algorithms are particularly suitable for oncology data analysis due to their flexibility and scalability to analyze diverse data types and strong computation power to learn underlying patterns from massive data sets. With the application of machine learning in analyzing TME, especially TAM’s traceable status, we could better understand the role of TAMs in tumor biology. Furthermore, we envision that the promotion of machine learning in this field could revolutionize tumor diagnosis, treatment stratification, and survival predictions in cancer research. In this article, we described key terms and concepts of machine learning, reviewed the applications of common methods in TAMs, and highlighted the challenges and future direction for TAMs in machine learning.

Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden

The purpose of this study was to investigate if radiomic analysis based on spectral micro-CT with nanoparticle contrast-enhancement can differentiate tumors based on lymphocyte burden. High mutational load transplant soft tissue sarcomas were initiated in Rag2^+/− and Rag2^−/− mice to model varying lymphocyte burden. Mice received radiation therapy (20 Gy) to the tumor-bearing hind limb and were injected with a liposomal iodinated contrast agent. Five days later, animals underwent conventional micro-CT imaging using an energy integrating detector (EID) and spectral micro-CT imaging using a photon-counting detector (PCD). Tumor volumes and iodine uptakes were measured. The radiomic features (RF) were grouped into feature-spaces corresponding to EID, PCD, and spectral decomposition images. The RFs were ranked to reduce redundancy and increase relevance based on TL burden. A stratified repeated cross validation strategy was used to assess separation using a logistic regression classifier. Tumor iodine concentration was the only significantly different conventional tumor metric between Rag2^+/− (TLs present) and Rag2^−/− (TL-deficient) tumors. The RFs further enabled differentiation between Rag2^+/− and Rag2^−/− tumors. The PCD-derived RFs provided the highest accuracy (0.68) followed by decomposition-derived RFs (0.60) and the EID-derived RFs (0.58). Such non-invasive approaches could aid in tumor stratification for cancer therapy studies.

Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based 18F-FDG PET/CT Radiomics

Accurate differentiation of intermediate/high mitosis-karyorrhexis index (MKI) from low MKI is vital for the further management of neuroblastoma. The purpose of this research was to investigate the efficacy of ¹⁸F-FDG PET/CT–based radiomics features for the prediction of MKI status of pediatric neuroblastoma via machine learning. A total of 102 pediatric neuroblastoma patients were retrospectively enrolled and divided into training (68 patients) and validation sets (34 patients) in a 2:1 ratio. Clinical characteristics and radiomics features were extracted by XGBoost algorithm and were used to establish radiomics and clinical models for MKI status prediction. A combined model was developed, encompassing clinical characteristics and radiomics features and presented as a radiomics nomogram. The predictive performance of the models was evaluated by AUC and decision curve analysis. The radiomics model yielded AUC of 0.982 (95% CI: 0.916, 0.999) and 0.955 (95% CI: 0.823, 0.997) in the training and validation sets, respectively. The clinical model yielded AUC of 0.746 and 0.670 in the training and validation sets, respectively. The combined model demonstrated AUC of 0.988 (95% CI: 0.924, 1.000) and 0.951 (95% CI: 0.818, 0.996) in the training and validation sets, respectively. The radiomics features could non-invasively predict MKI status of pediatric neuroblastoma with high accuracy.

Radiogenomics: Hunting Down Liver Metastasis in Colorectal Cancer Patients

Simple Summary

Colorectal cancer (CRC) is the third leading cause of cancer and the second most deadly tumor type in the world. The liver is the most common site of metastasis in CRC patients. The conversion of new imaging biomarkers into diagnostic, prognostic and predictive signatures, by the development of radiomics and radiogenomics, is an important potential new tool for the clinical management of cancer patients. In this review, we assess the knowledge gained from radiomics and radiogenomics studies in liver metastatic colorectal cancer patients and their possible use for early diagnosis, response assessment and treatment decisions.

Abstract

Radiomics is a developing new discipline that analyzes conventional medical images to extract quantifiable data that can be mined for new biomarkers that show the biology of pathological processes at microscopic levels. These data can be converted into image-based signatures to improve diagnostic, prognostic and predictive accuracy in cancer patients. The combination of radiomics and molecular data, called radiogenomics, has clear implications for cancer patients’ management. Though some studies have focused on radiogenomics signatures in hepatocellular carcinoma patients, only a few have examined colorectal cancer metastatic lesions in the liver. Moreover, the need to differentiate between liver lesions is fundamental for accurate diagnosis and treatment. In this review, we summarize the knowledge gained from radiomics and radiogenomics studies in hepatic metastatic colorectal cancer patients and their use in early diagnosis, response assessment and treatment decisions. We also investigate their value as possible prognostic biomarkers. In addition, the great potential of image mining to provide a comprehensive view of liver niche formation is examined thoroughly. Finally, new challenges and current limitations for the early detection of the liver premetastatic niche, based on radiomics and radiogenomics, are also discussed.