A tumor vasculature-based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors

Magnetic resonance imaging-based approaches for detecting the efficacy of combining therapy following VEGFR-2 and PD-1 blockade in a colon cancer model

BACKGROUND

Angiogenesis inhibitors have been identified to improve the efficacy of immunotherapy in recent studies. However, the delayed therapeutic effect of immunotherapy poses challenges in treatment planning. Therefore, this study aims to explore the potential of non-invasive imaging techniques, specifically intravoxel-incoherent-motion diffusion-weighted imaging (IVIM-DWI) and blood oxygenation level-dependent magnetic resonance imaging (BOLD-MRI), in detecting the anti-tumor response to the combination therapy involving immune checkpoint blockade therapy and anti-angiogenesis therapy in a tumor-bearing animal model.

METHODS

The C57BL/6 mice were implanted with murine MC-38 cells to establish colon cancer xenograft model, and randomly divided into the control group, anti-PD-1 therapy group, and combination therapy group (VEGFR-2 inhibitor combined with anti-PD-1 antibody treatment). All mice were imaged before and, on the 3rd, 6th, 9th, and 12th day after administration, and pathological examinations were conducted at the same time points.

RESULTS

The combination therapy group effectively suppressed tumor growth, exhibiting a significantly higher tumor inhibition rate of 69.96% compared to the anti-PD-1 group (56.71%). The f value and D* value of IVIM-DWI exhibit advantages in reflecting tumor angiogenesis. The D* value showed the highest correlation with CD31 (r = 0.702, P = 0.001), and the f value demonstrated the closest correlation with vessel maturity (r = 0.693, P = 0.001). While the BOLD-MRI parameter, R2* value, shows the highest correlation with Hif-1α(r = 0.778, P < 0.001), indicating the capability of BOLD-MRI to evaluate tumor hypoxia. In addition, the D value of IVIM-DWI is closely related to tumor cell proliferation, apoptosis, and infiltration of lymphocytes. The D value was highly correlated with Ki-67 (r = - 0.792, P < 0.001), TUNEL (r = 0.910, P < 0.001) and CD8a (r = 0.918, P < 0.001).

CONCLUSIONS

The combination of VEGFR-2 inhibitors with PD-1 immunotherapy shows a synergistic anti-tumor effect on the mouse colon cancer model. IVIM-DWI and BOLD-MRI are expected to be used as non-invasive approaches to provide imaging-based evidence for tumor response detection and efficacy evaluation.

The Unseen Hand: AI-Based Prescribing Decision Support Tools and the Evaluation of Drug Safety and Effectiveness

The use of artificial intelligence (AI)-based tools to guide prescribing decisions is full of promise and may enhance patient outcomes. These tools can perform actions such as choosing the 'safest' medication, choosing between competing medications, promoting de-prescribing or even predicting non-adherence. These tools can exist in a variety of formats; for example, they may be directly integrated into electronic medical records or they may exist in a stand-alone website accessible by a web browser. One potential impact of these tools is that they could manipulate our understanding of the benefit-risk of medicines in the real world. Currently, the benefit risk of approved medications is assessed according to carefully planned agreements covering spontaneous reporting systems and planned surveillance studies. But AI-based tools may limit or even block prescription to high-risk patients or prevent off-label use. The uptake and temporal availability of these tools may be uneven across healthcare systems and geographies, creating artefacts in data that are difficult to account for. It is also hard to estimate the 'true impact' that a tool had on a prescribing decision. International borders may also be highly porous to these tools, especially in cases where tools are available over the web. These tools already exist, and their use is likely to increase in the coming years. How they can be accounted for in benefit-risk decisions is yet to be seen.

Tackling the immunotherapy conundrum: advances and challenges for operable non-small-cell lung cancer treatment

Lung cancer is the most common cause of cancer-related deaths worldwide. Non-small-cell lung cancer (NSCLC) represents the majority of lung cancer cases, and its standard treatment is primarily surgery. Nonetheless, this type of cancer exhibits an important rate of tumor recurrence. Immune checkpoint inhibitors (ICIs) have demonstrated significant survival benefits in many cancers, especially in early-stage NSCLC. This review considers the latest CheckMate816, IMpower010 and KEYNOTE-091 trials that led to US FDA approvals. The new wave of resectable NSCLC trial results are also summarized. Finally, the latest challenges for these treatment modalities, such as the choice between neoadjuvant and adjuvant use, the accurate identification of biomarkers and the presence of driver mutations such as EGFR, are discussed.

Nanoprobe-based molecular imaging for tumor stratification

The responses of patients to tumor therapies vary due to tumor heterogeneity. Tumor stratification has been attracting increasing attention for accurately distinguishing between responders to treatment and non-responders. Nanoprobes with unique physical and chemical properties have great potential for patient stratification. This review begins by describing the features and design principles of nanoprobes that can visualize specific cell types and biomarkers and release inflammatory factors during or before tumor treatment. Then, we focus on the recent advancements in using nanoprobes to stratify various therapeutic modalities, including chemotherapy, radiotherapy (RT), photothermal therapy (PTT), photodynamic therapy (PDT), chemodynamic therapy (CDT), ferroptosis, and immunotherapy. The main challenges and perspectives of nanoprobes in cancer stratification are also discussed to facilitate probe development and clinical applications.

Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment

Recently, a wide spectrum of artificial intelligence (AI)-based applications in the broader categories of digital pathology, biomarker development, and treatment have been explored. In the domain of digital pathology, these have included novel analytical strategies for realizing new information derived from standard histology to guide treatment selection and biomarker development to predict treatment selection and response. In therapeutics, these have included AI-driven drug target discovery, drug design and repurposing, combination regimen optimization, modulated dosing, and beyond. Given the continued advances that are emerging, it is important to develop workflows that seamlessly combine the various segments of AI innovation to comprehensively augment the diagnostic and interventional arsenal of the clinical oncology community. To overcome challenges that remain with regard to the ideation, validation, and deployment of AI in clinical oncology, recommendations toward bringing this workflow to fruition are also provided from clinical, engineering, implementation, and health care economics considerations. Ultimately, this work proposes frameworks that can potentially integrate these domains toward the sustainable adoption of practice-changing AI by the clinical oncology community to drive improved patient outcomes.