Computed Tomography–Derived Radiomic Metrics Can Identify Responders to Immunotherapy in Ovarian Cancer

Himoto, Yuki; Veeraraghavan, Harini; Zheng, Junting; Zamarin, Dmitriy; Snyder, Alexandra; Capanu, Marinela; Nougaret, Stéphanie; Vargas, Hebert Alberto; Shitano, Fuki; Callahan, Margaret K.; Wang, Wei; Sala, Evis; Lakhman, Yulia

doi:10.1200/po.19.00038

Cited by 19 publications

(18 citation statements)

References 35 publications

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“…Due to the small sample size, there are inevitable biases in extracting CT textural features, with the risk of both false-positive and false-negative results. Therefore, we a priori selected only three texture metrics to assess IISTH, which had already been validated in larger studies to be predictive of treatment response and survival in patients with HGSOC [18,19]. In addition, the process of selecting proteins may have screened out proteins that may be important.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, CT radiomic features of patients with ovarian cancer correlate with response to therapy [13], progression-free survival [14,15], and overall survival [15], and can identify patients at higher risk for recurrence [16]. Recent work by our group focused on evaluating the possible associations between CT imaging traits and texture metrics with genomics data and patient outcome [17][18][19]. The integration of clinical, proteomic, and radiomic data may enable to stratify patients according to risk for progression thereby allowing for tailored therapy [20].…”

Section: Introductionmentioning

confidence: 99%

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Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis

et al. 2020

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Objectives To investigate the association between CT imaging traits and texture metrics with proteomic data in patients with high-grade serous ovarian cancer (HGSOC). Methods This retrospective, hypothesis-generating study included 20 patients with HGSOC prior to primary cytoreductive surgery. Two readers independently assessed the contrast-enhanced computed tomography (CT) images and extracted 33 imaging traits, with a third reader adjudicating in the event of a disagreement. In addition, all sites of suspected HGSOC were manually segmented texture features which were computed from each tumor site. Three texture features that represented intra-and inter-site tumor heterogeneity were used for analysis. An integrated analysis of transcriptomic and proteomic data identified proteins with conserved expression between primary tumor sites and metastasis. Correlations between protein abundance and various CT imaging traits and texture features were assessed using the Kendall tau rank correlation coefficient and the Mann-Whitney U test, whereas the area under the receiver operating characteristic curve (AUC) was reported as a metric of the strength and the direction of the association. P values < 0.05 were considered significant. Results Four proteins were associated with CT-based imaging traits, with the strongest correlation observed between the CRIP2 protein and disease in the mesentery (p < 0.001, AUC = 0.05). The abundance of three proteins was associated with texture features that represented intra-and inter-site tumor heterogeneity, with the strongest negative correlation between the CKB protein and cluster dissimilarity (p = 0.047, τ = 0.326). Conclusion This study provides the first insights into the potential associations between standard-of-care CT imaging traits and texture measures of intra-and inter-site heterogeneity, and the abundance of several proteins. Key Points • CT-based texture features of intra-and inter-site tumor heterogeneity correlate with the abundance of several proteins in patients with HGSOC. • CT imaging traits correlate with protein abundance in patients with HGSOC.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis

et al. 2020

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“…However, the model statistically significantly predicted OS in both tumor types (NSCLC: AUC: 0.76, p < 0.01; melanoma: AUC: 0.77, p < 0.01) [197]. Correlations of CT-based radiomic features and therapy response were also reported for patients with advanced ovarian cancer [198] and bladder cancer [199] undergoing immune-checkpoint blockade. Table 6 summarizes radiomics studies predicting clinical outcome with immune-checkpoint blockade.…”

Section: Radiomics Predict Clinical Outcome With Ici Therapymentioning

confidence: 82%

“…Response prediction of individual metastases and OS prediction based on multiple radiomic features [198] Himoto et al ovarian cancer…”

Section: Melanoma Nsclcmentioning

confidence: 99%

Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence

Huemer

Leisch

Geisberger³

et al. 2020

IJMS

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The therapeutic concept of unleashing a pre-existing immune response against the tumor by the application of immune-checkpoint inhibitors (ICI) has resulted in long-term survival in advanced cancer patient subgroups. However, the majority of patients do not benefit from single-agent ICI and therefore new combination strategies are eagerly necessitated. In addition to conventional chemotherapy, kinase inhibitors as well as tumor-specific vaccinations are extensively investigated in combination with ICI to augment therapy responses. An unprecedented clinical outcome with chimeric antigen receptor (CAR-)T cell therapy has led to the approval for relapsed/refractory diffuse large B cell lymphoma and B cell acute lymphoblastic leukemia whereas response rates in solid tumors are unsatisfactory. Immune-checkpoints negatively impact CAR-T cell therapy in hematologic and solid malignancies and as a consequence provide a therapeutic target to overcome resistance. Established biomarkers such as programmed death ligand 1 (PD-L1) and tumor mutational burden (TMB) help to select patients who will benefit most from ICI, however, biomarker negativity does not exclude responses. Investigating alterations in the antigen presenting pathway as well as radiomics have the potential to determine tumor immunogenicity and response to ICI. Within this review we summarize the literature about specific combination partners for ICI and the applicability of artificial intelligence to predict ICI therapy responses.

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“…9 10 Furthermore, marked intratumor and intertumor genetic and microenvironment heterogeneity presents an additional layer of complexity 11 12 and may contribute to the lack of ICI response. 13 Vaccination against tumor-associated antigens (TAAs) is a potential strategy to increase the therapeutic efficacy of ICIs through development or enhancement of antitumor T cell responses. Folate receptor Open access alpha (FRα), also known as folate receptor 1 (FOLR1), is a glycosylphosphatidylinositol-linked protein which participates in embryonic neural tube formation.…”

Section: Introductionmentioning

confidence: 99%

Safety, immunogenicity, and clinical efficacy of durvalumab in combination with folate receptor alpha vaccine TPIV200 in patients with advanced ovarian cancer: a phase II trial

Zamarin

Walderich

Holland

et al. 2020

J Immunother Cancer

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BackgroundImmune checkpoint inhibitors (ICIs) to date have demonstrated limited activity in advanced ovarian cancer (OC). Folate receptor alpha (FRα) is overexpressed in the majority of OCs and presents an attractive target for a combination immunotherapy to potentially overcome resistance to ICI in OCs. The current study sought to examine clinical and immunologic responses to TPIV200, a multiepitope FRα vaccine administered with programmed death ligand 1 (PD-L1) inhibitor durvalumab in patients with advanced platinum-resistant OC.MethodsFollowing Simon two-stage phase II trial design, 27 patients were enrolled. Treatment was administered in 28-day cycles (intradermal TPIV200 and granulocyte-macrophage colony-stimulating factor (GM-CSF) for 6 cycles and intravenous durvalumab for 12 cycles). Primary endpoints included overall response rate and progression-free survival at 24 weeks. Translational parameters focused on tumor microenvironment, PD-L1 and FRα expression, and peripheral vaccine-specific immune responses.ResultsTreatment was well tolerated, with related grade 3 toxicity rate of 18.5%. Increased T cell responses to the majority of peptides were observed in all patients at 6 weeks (p<0.0001). There was one unconfirmed partial response (3.7%) and nine patients had stable disease (33.3%). Clinical benefit was not associated with baseline FRα or PD-L1 expression. One patient with prolonged clinical benefit demonstrated loss of FRα expression and upregulation of PD-L1 in a progressing lesion. Despite the low overall response rate, the median overall survival was 21 months (13.5–∞), with evidence of benefit from postimmunotherapy regimens.ConclusionsCombination of TPIV200 and durvalumab was safe and elicited robust FRα-specific T cell responses in all patients. Unexpectedly durable survival in this heavily pretreated population highlights the need to investigate the impact of FRα vaccination on the OC biology post-treatment.

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Computed Tomography–Derived Radiomic Metrics Can Identify Responders to Immunotherapy in Ovarian Cancer

Cited by 19 publications

References 35 publications

Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis

Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis

Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence

Safety, immunogenicity, and clinical efficacy of durvalumab in combination with folate receptor alpha vaccine TPIV200 in patients with advanced ovarian cancer: a phase II trial

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