Radiomic biomarkers of tumor immune biology and immunotherapy response.
Ontology highlight
ABSTRACT:
SUBMITTER: Wang JH
PROVIDER: S-EPMC8076712 | biostudies-literature | 2021 May
REPOSITORIES: biostudies-literature
Publications
Radiomic biomarkers of tumor immune biology and immunotherapy response.
Wang Jarey H JH Wahid Kareem A KA van Dijk Lisanne V LV Farahani Keyvan K Thompson Reid F RF Fuller Clifton David CD
Clinical and translational radiation oncology 20210407
Immunotherapies are leading to improved outcomes for many cancers, including those with devastating prognoses. As therapies like immune checkpoint inhibitors (ICI) become a mainstay in treatment regimens, many concurrent challenges have arisen - for instance, delineating clinical responders from non-responders. Predicting response has proven to be difficult given a lack of consistent and accurate biomarkers, heterogeneity of the tumor microenvironment (TME), and a poor understanding of resistanc ...[more]
Similar Datasets
Project description:IntroductionImmunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds-urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algorithms can automatically quantify radiographic characteristics that are related to and may therefore act as noninvasive radiomic biomarkers for immunotherapy response.Patients and methodsIn this study, we analyzed 1055 primary and metastatic lesions from 203 patients with advanced melanoma and non-small-cell lung cancer (NSCLC) undergoing anti-PD1 therapy. We carried out an AI-based characterization of each lesion on the pretreatment contrast-enhanced CT imaging data to develop and validate a noninvasive machine learning biomarker capable of distinguishing between immunotherapy responding and nonresponding. To define the biological basis of the radiographic biomarker, we carried out gene set enrichment analysis in an independent dataset of 262 NSCLC patients.ResultsThe biomarker reached significant performance on NSCLC lesions (up to 0.83 AUC, P < 0.001) and borderline significant for melanoma lymph nodes (0.64 AUC, P = 0.05). Combining these lesion-wide predictions on a patient level, immunotherapy response could be predicted with an AUC of up to 0.76 for both cancer types (P < 0.001), resulting in a 1-year survival difference of 24% (P = 0.02). We found highly significant associations with pathways involved in mitosis, indicating a relationship between increased proliferative potential and preferential response to immunotherapy.ConclusionsThese results indicate that radiographic characteristics of lesions on standard-of-care imaging may function as noninvasive biomarkers for response to immunotherapy, and may show utility for improved patient stratification in both neoadjuvant and palliative settings.
| S-EPMC6594459 | biostudies-literature
Project description:Cancers are highly complex diseases that are characterized by not only the overgrowth of malignant cells but also an altered immune response. The inhibition and reprogramming of the immune system play critical roles in tumor initiation and progression. Immunotherapy aims to reactivate antitumor immune cells and overcome the immune escape mechanisms of tumors. Represented by immune checkpoint blockade and adoptive cell transfer, tumor immunotherapy has seen tremendous success in the clinic, with the capability to induce long-term regression of some tumors that are refractory to all other treatments. Among them, immune checkpoint blocking therapy, represented by PD-1/PD-L1 inhibitors (nivolumab) and CTLA-4 inhibitors (ipilimumab), has shown encouraging therapeutic effects in the treatment of various malignant tumors, such as non-small cell lung cancer (NSCLC) and melanoma. In addition, with the advent of CAR-T, CAR-M and other novel immunotherapy methods, immunotherapy has entered a new era. At present, evidence indicates that the combination of multiple immunotherapy methods may be one way to improve the therapeutic effect. However, the overall clinical response rate of tumor immunotherapy still needs improvement, which warrants the development of novel therapeutic designs as well as the discovery of biomarkers that can guide the prescription of these agents. Learning from the past success and failure of both clinical and basic research is critical for the rational design of studies in the future. In this article, we describe the efforts to manipulate the immune system against cancer and discuss different targets and cell types that can be exploited to promote the antitumor immune response.
| S-EPMC9485144 | biostudies-literature
Project description:Remarkable developments in immuno-oncology have changed the landscape of gastric cancer (GC) treatment. Because immunotherapy intervenes with tumor immune response rather than directly targeting tumor cells, it is important to develop a greater understanding of tumor immunity. This review paper summarizes the tumor immune reaction and immune escape mechanisms while focusing on the role of T cells and their co-inhibitory signals, such as the immune checkpoint molecules programmed death-1 and programmed deathligand 1 (PD-L1). This paper also describes past clinical trials of immunotherapy for patients with GC and details their clinical implications. Strong predictive markers are essential to improve response to immunotherapy. Microsatellite instability, Epstein-Barr virus, PD-L1 expression, and tumor mutational burden are now regarded as potent predictive markers for immunotherapy in patients with GC. Novel immunotherapy and combination therapy targeting new immune checkpoint molecules such as lymphocyte-activation gene 3, T cell immunoglobulin, and mucin domain containing-3, and indoleamine 2,3-dioxygenase have been suggested, and trials are ongoing to evaluate their safety and efficacy. Immunotherapy is an important treatment option for patients with GC and has great potential for improving patient outcome, and further research in immuno-oncology should be carried out.
| S-EPMC6986974 | biostudies-literature
Project description:The therapeutic activation of antitumour immunity by immune checkpoint inhibitors (ICIs) is a significant advance in cancer medicine, not least due to the prospect of long-term remission. However, many patients are unresponsive to ICI therapy and may experience serious side effects; companion biomarkers are urgently needed to help inform ICI prescribing decisions. We present the IMMUNETS networks of gene coregulation in five key immune cell types and their application to interrogate control of nivolumab response in advanced melanoma cohorts. The results evidence a role for each of the IMMUNETS cell types in ICI response and in driving tumour clearance with independent cohorts from TCGA. As expected, 'immune hot' status, including T cell proliferation, correlates with response to first-line ICI therapy. Genes regulated in NK, dendritic, and B cells are the most prominent discriminators of nivolumab response in patients that had previously progressed on another ICI. Multivariate analysis controlling for tumour stage and age highlights CIITA and IKZF3 as candidate prognostic biomarkers. IMMUNETS provide a resource for network biology, enabling context-specific analysis of immune components in orthogonal datasets. Overall, our results illuminate the relationship between the tumour microenvironment and clinical trajectories, with potential implications for precision medicine.
| S-EPMC9225330 | biostudies-literature
Project description:IntroductionsIdentifying patients with non-small cell lung cancer (NSCLC) who are optimal candidates for immunotherapy is a cornerstone in clinical decision-making. The tumor immune microenvironment (TIME) is intricately linked with both the prognosis of the malignancy and the efficacy of immunotherapeutic interventions. CD8+ T cells, and more specifically, tissue-resident memory CD8+ T cells [CD8+ tissue-resident memory T (TRM) cells] are postulated to be pivotal in orchestrating the immune system's assault on tumor cells. Nevertheless, the accurate quantification of immune cell infiltration-and by extension, the prediction of immunotherapeutic efficacy-remains a significant scientific frontier.MethodsIn this study, we introduce a cutting-edge non-invasive radiomic model, grounded in TIME markers (CD3+ T, CD8+ T, and CD8+ TRM cells), to infer the levels of immune cell infiltration in NSCLC patients receiving immune checkpoint inhibitors and ultimately predict their response to immunotherapy. Data from patients who had surgical resections (cohort 1) were employed to construct a radiomic model capable of predicting the TIME. This model was then applied to forecast the TIME for patients under immunotherapy (cohort 2). Conclusively, the study delved into the association between the predicted TIME from the radiomic model and the immunotherapeutic outcomes of the patients.ResultFor the immune cell infiltration radiomic prediction models in cohort 1, the AUC values achieved 0.765, 0.763, and 0.675 in the test set of CD3+ T, CD8+ T, and CD8+ TRM, respectively. While the AUC values for the TIME-immunotherapy predictive value were 0.651, 0.763, and 0.829 in the CD3-immunotherapy response model, CD8-immunotherapy response model, and CD8+ TRM-immunotherapy response model in cohort 2, respectively. The CD8+ TRM-immunotherapy model exhibited the highest predictive value and was significantly better than the CD3-immunotherapy model in predicting the immunotherapy response. The progression-free survival (PFS) analysis based on the predicted levels of CD3+ T, CD8+ T, and CD8+ TRM immune cell infiltration showed that the CD8+ T cell infiltration level was an independent factor (P=0.014, HR=0.218) with an AUC value of 0.938.DiscussionOur empirical evidence reveals that patients with substantial CD8+ T cell infiltration experience a markedly improved PFS compared with those with minimal infiltration, asserting the status of the CD8+ T cell as an independent prognosticator of PFS in the context of immunotherapy. Although CD8+ TRM cells demonstrated the greatest predictive accuracy for immunotherapy response, their predictive strength for PFS was marginally surpassed by that of CD8+ T cells. These insights advocate for the application of the proposed non-invasive radiomic model, which utilizes TIME analysis, as a reliable predictor for immunotherapy outcomes and PFS in NSCLC patients.
| S-EPMC11416977 | biostudies-literature
Project description:ObjectivesTo develop and validate a radiomics model for evaluating treatment response to immune-checkpoint inhibitor plus chemotherapy (ICI + CT) in patients with advanced esophageal squamous cell carcinoma (ESCC).MethodsA total of 64 patients with advance ESCC receiving first-line ICI + CT at two centers between January 2019 and June 2020 were enrolled in this study. Both 2D ROIs and 3D ROIs were segmented. ComBat correction was applied to minimize the potential bias on the results due to different scan protocols. A total of 788 features were extracted and radiomics models were built on corrected/uncorrected 2D and 3D features by using 5-fold cross-validation. The performance of the radiomics models was assessed by its discrimination, calibration and clinical usefulness with independent validation.ResultsFive features and support vector machine algorithm were selected to build the 2D uncorrected, 2D corrected, 3D uncorrected and 3D corrected radiomics models. The 2D radiomics models significantly outperformed the 3D radiomics models in both primary and validation cohorts. When ComBat correction was used, the performance of 2D models was better (p = 0.0059) in the training cohort, and significantly better (p < 0.0001) in the validation cohort. The 2D corrected radiomics model yielded the optimal performance and was used to build the nomogram. The calibration curve of the radiomics model demonstrated good agreement between prediction and observation and the decision curve analysis confirmed the clinical utility.ConclusionsThe easy-to-use 2D corrected radiomics model could facilitate noninvasive preselection of ESCC patients who would benefit from ICI + CT.
| S-EPMC8557514 | biostudies-literature
Project description:BackgroundImmunotherapies, such as programmed death 1/programmed death ligand 1 (PD-1/PD-L1) antibodies have been shown to improve overall and progression-free survival (PFS) in patients with locally advanced or metastatic non-small cell lung cancer (NSCLC). However, not all patients derive a meaningful clinical benefit. Additionally, patients receiving anti-PD-1/PD-L1 therapy can experience immune-related adverse events (irAEs). Clinically significant irAEs may require temporary pause or discontinuation of treatment. Having a tool to identify patients who may not benefit and/or are at risk for developing severe irAEs from immunotherapy will aid in an informed decision-making process for the patients and their physicians.MethodsComputed tomography (CT) scans and clinical data were retrospectively collected for this study to develop three prediction models using (I) radiomic features, (II) clinical features, and (III) radiomic and clinical features combined. Each subject had 6 clinical features and 849 radiomic features extracted. Selected features were run through an artificial neural network (NN) trained on 70% of the cohort, maintaining the case and control ratio. The NN was assessed by calculating the area-under-the-receiver-operating-characteristic curve (AUC-ROC), area-under-the-precision-recall curve (AUC-PR), sensitivity, and specificity.ResultsA cohort of 132 subjects, of which 43 (33%) had a PFS ≤90 days and 89 (67%) of which had a PFS >90 days was used to develop the prediction models. The radiomic model was able to predict progression-free survival with a training AUC-ROC of 87% and testing AUC-ROC, sensitivity, and specificity of 83%, 75%, and 81%, respectively. In this cohort, the clinical and radiomic combined features did add a slight increase in the specificity (85%) but with a decrease in sensitivity (75%) and AUC-ROC (81%).ConclusionsWhole lung segmentation and feature extraction can identify those that would see a benefit from anti-PD-1/PD-L1 therapy.
| S-EPMC10261870 | biostudies-literature
Project description:The advancement of cancer immunotherapy faces barriers which limit its efficacy. These include weak immunogenicity of the tumor, as well as immunosuppressive mechanisms which prevent effective antitumor immune responses. Recent studies suggest that aberrant expression of cancer testis antigens (CTAs) can generate robust antitumor immune responses, which implicates CTAs as potential targets for immunotherapy. However, the heterogeneity of tumor cells in the presence and quantity of CTA expression results in tumor escape from CTA-specific immune responses. Thus, the ability to modulate the tumor cell epigenome to homogenously induce expression of such antigens will likely render the tumor more immunogenic. Additionally, emerging studies suggest that suppression of antitumor immune responses may be overcome by reprogramming innate and adaptive immune cells. Therefore, this paper discusses recent studies which address barriers to successful cancer immunotherapy and proposes a strategy of modulation of tumor-immune cell crosstalk to improve responses in carcinoma patients.
| S-EPMC3477552 | biostudies-other
Project description:BackgroundCancer of unknown primary (CUP) is a heterogeneous group of metastatic cancers where a primary tissue of origin (TOO) is uncertain. Most patients with CUP have limited treatment options and poor survival outcomes. Immune checkpoint inhibitors (ICIs) can be efficacious in some patients with CUP, but the optimal predictive biomarkers are unknown. We therefore assessed immune and genomic biomarkers as well as predicted TOO in patients with CUP, including a subset treated with ICIs.MethodsPatients with CUP were subject to gene-expression profiling (GEP) and DNA panel sequencing. Immune and stromal-related gene expression was explored by NanoString, including genes associated with immunotherapy response (IR) in other solid malignancies. ICI responsive cancer types were assigned based on Food and Drug Administration-approved indications, and either detection of a latent primary tumor or the TOO was suspected based on genomics informed pathology review. Tumor mutation burden (TMB) and gene mutations were also assessed.ResultsA total of 219 patients with CUP were included, 215 assessed for TOO in a previous study, with the majority (163) receiving both RNA and DNA tests. Of GEP profiled cases, 33% (59/175) had a high IR gene-expression score. Of the DNA sequenced cases, 16% (32/203) had high TMB (>10 mutations/Mb), including two with mismatch repair deficiency. Low correlation was observed between TMB and an IR score (R=0.26, p<0.001). Among 110 CUPs with a latent primary or suspected TOO, 47% (52/110) belonged to ICI-responsive cancer types. More than half of the CUPs had at least one feature that may predict ICI response (high IR score, high TMB, ICI-responsive cancer type). Among patients with CUP treated with ICIs, 8/28 (29%) responded (2 complete responses and 6 partial responses). Among non-responders, 9 had stable and 11 had progressive disease. All responders had a high IR score (7/8) and/or high TMB (3/8), while most (5/8) belonged to ICI-responsive cancer types. These features were detected at a lower frequency in non-responders and mostly in patients with stable disease.ConclusionsA significant fraction of CUP tumors had genomic features previously associated with ICI response. High IR score was the most sensitive predictive feature of ICI response, warranting evaluation in a larger patient series.
| S-EPMC10098268 | biostudies-literature
Project description:Immune checkpoint blockade (ICB) therapy has yielded successful clinical responses in treatment of a minority of patients in certain cancer types. Substantial efforts were made to establish biomarkers for predicting responsiveness to ICB. However, the systematic assessment of these ICB response biomarkers remains insufficient. We collected 22 transcriptome-based biomarkers for ICB response and constructed multiple benchmark datasets to evaluate the associations with clinical response, predictive performance, and clinical efficacy of them in pre-treatment patients with distinct ICB agents in diverse cancers. Overall, "Immune-checkpoint molecule" biomarkers PD-L1, PD-L2, CTLA-4 and IMPRES and the "Effector molecule" biomarker CYT showed significant associations with ICB response and clinical outcomes. These immune-checkpoint biomarkers and another immune effector IFN-gamma presented predictive ability in melanoma, urothelial cancer (UC) and clear cell renal-cell cancer (ccRCC). In non-small cell lung cancer (NSCLC), only PD-L2 and CTLA-4 showed preferable correlation with clinical response. Under different ICB therapies, the top-performing biomarkers were usually mutually exclusive in patients with anti-PD-1 and anti-CTLA-4 therapy, and most of biomarkers presented outstanding predictive power in patients with combined anti-PD-1 and anti-CTLA-4 therapy. Our results show these biomarkers had different performance in predicting ICB response across distinct ICB agents in diverse cancers.
| S-EPMC8037221 | biostudies-literature