The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.
Surgery, radiation, hormonal therapy, chemotherapy, and immunotherapy are standard treatment strategies for metastatic breast cancer. However, the heterogeneous nature of the disease poses challenges and continues to make it life-threatening....
Association of HLA class I homozygosity with unfavorable clinical outcomes in patients with non-small cell lung cancer treated with chemo-immunotherapy or immunotherapy as first-line therapy, HELIYON, https://doi.org/10.1016/j.heliyon.2021.e07916. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
As the use of immune checkpoint inhibitors (ICIs) in treating a variety of cancer types has increased in recent years, so too have the number of reports on patients acquiring resistance to these therapies. Overcoming acquired resistance to immunotherapy remains an important need in the field of immuno-oncology. Herein, we present a case that suggests sequential administration of combination immunotherapy may be beneficial to advanced cervical cancer patients exhibiting acquired resistance to mono-immunotherapy. The patient’s tumor is microsatellite instability-high (MSI-H), which is an important biomarker in predicting ICI response. Results from recent interim prospective studies using combination immunotherapy (eg, nivolumab and ipilimumab) with anti-PD-1 plus anti-CTLA-4 inhibitor following progression on anti-PD-1 inhibitors (eg, nivolumab) have shown anti-tumor activity in patients with advanced melanoma and metastatic urothelial carcinoma. We also introduce retrospective studies and case reports/case series of dual checkpoint inhibition with anti-PD-1 inhibitor plus anti-CTLA-4 inhibitor after progression on prior anti-PD/PD-L1 monotherapy. To date, there has been no prospective study on the use of combined anti-PD-1 and anti-CTLA-4 therapy at the time of progression on anti-PD-1 therapy in patients with MSI-H tumors or advanced cervical cancer. In this report, we provide evidence that supports future investigations into such treatments.
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