Circulating tumor cells (CTCs) have been recognized as a major contributor to distant metastasis. Their unique role as metastatic seeds renders them a potential marker in the circulation for early cancer diagnosis and prognosis as well as monitoring of therapeutic response. In the past decade, researchers mainly focused on the development of isolation techniques for improving the recovery rate and purity of CTCs. These developed techniques have significantly increased the detection sensitivity and enumeration accuracy of CTCs. Currently, significant efforts have been made toward comprehensive molecular characterization, ex vivo expansion of CTCs, and understanding the interactions between CTCs and their associated cells (e.g., immune cells and stromal cells) in the circulation. In this review, we briefly summarize existing CTC isolation technologies and specifically focus on advances in downstream analysis of CTCs and their potential applications in precision medicine. We also discuss the current challenges and future opportunities in their clinical utilization.

There are currently 5 million to 10 million human T-lymphotropic virus type 1 (HTLV-1)-infected people, and many of them will develop severe complications resulting from this infection. A vaccine is urgently needed in areas where HTLV-1 is endemic. Many vaccines are best tested in nonhuman primate animal models. As a first step in designing an effective HTLV-1 vaccine, we defined the CD8

Purpose Immunotherapy (IT) and radiotherapy (RT) can act synergistically, enhancing antitumor response beyond what either treatment can achieve separately. Anecdotal reports suggest that these results are in part due to the induction of an abscopal effect on non-irradiated lesions. Systematic data on incidence of the abscopal effect are scarce, while the existence and the identification of predictive signatures or this phenomenon are lacking. The purpose of this pre-clinical investigational work is to shed more light on the subject by identifying several imaging features and blood counts, which can be utilized to build a predictive binary logistic model. Materials and methods This proof-of-principle study was performed on Lewis Lung Carcinoma in a syngeneic, subcutaneous murine model. Nineteen mice were used: four as control and the rest were subjected to combined RT plus IT regimen. Tumors were implanted on both flanks and after reaching volume of ~200 mm3 the animals were CT and MRI imaged and blood was collected. Quantitative imaging features (radiomics) were extracted for both flanks. Subsequently, the treated animals received radiation (only to the right flank) in three 8 Gy fractions followed by PD-1 inhibitor administrations. Tumor volumes were followed and animals exhibiting identical of better tumor growth delay on the non-irradiated (left) flank as compared to the irradiated flank were identified as experiencing an abscopal effect. Binary logistic regression analysis was performed to create models for CT and MRI radiomics and blood counts, which are predictive of the abscopal effect. Results Four of the treated animals experienced an abscopal effect. Three CT and two MRI radiomics features together with the pre-treatment neutrophil-to-lymphocyte (NLR) ratio correlated with the abscopal effect. Predictive models were created by combining the radiomics with NLR. ROC analyses indicated that the CT model had AUC of 0.846, while the MRI model had AUC of 0.946. Conclusions The combination of CT and MRI radiomics with blood counts resulted in models with AUCs of 1 on the modeling dataset. Application of the models to the validation dataset exhibited AUCs above 0.84, indicating very good predictive power of the combination between quantitative imaging and blood counts.

Purpose: Combined radiotherapy (RT) and immune checkpoint-inhibitor (ICI) therapy can act synergistically to enhance tumor response beyond what either treatment can achieve alone. Alongside the revolutionary impact of ICIs on cancer therapy, life-threatening potential side effects, such as checkpoint-inhibitor-induced (CIP) pneumonitis, remain underreported and unpredictable. In this preclinical study, we hypothesized that routinely collected data such as imaging, blood counts, and blood cytokine levels can be utilized to build a model that predicts lung inflammation associated with combined RT/ICI therapy. Materials and Methods: This proof-of-concept investigational work was performed on Lewis lung carcinoma in a syngeneic murine model. Nineteen mice were used, four as untreated controls and the rest subjected to RT/ICI therapy. Tumors were implanted subcutaneously in both flanks and upon reaching volumes of ~200 mm3 the animals were imaged with both CT and MRI and blood was collected. Quantitative radiomics features were extracted from imaging of both lungs. The animals then received RT to the right flank tumor only with a regimen of three 8 Gy fractions (one fraction per day over 3 days) with PD-1 inhibitor administration delivered intraperitoneally after each daily RT fraction. Tumor volume evolution was followed until tumors reached the maximum size allowed by the Institutional Animal Care and Use Committee (IACUC). The animals were sacrificed, and lung tissues harvested for immunohistochemistry evaluation. Tissue biomarkers of lung inflammation (CD45) were tallied, and binary logistic regression analyses were performed to create models predictive of lung inflammation, incorporating pretreatment CT/MRI radiomics, blood counts, and blood cytokines. Results: The treated animal cohort was dichotomized by the median value of CD45 infiltration in the lungs. Four pretreatment radiomics features (3 CT features and 1 MRI feature) together with pre-treatment neutrophil-to-lymphocyte (NLR) ratio and pre-treatment granulocyte-macrophage colony-stimulating factor (GM-CSF) level correlated with dichotomized CD45 infiltration. Predictive models were created by combining radiomics with NLR and GM-CSF. Receiver operating characteristic (ROC) analyses of two-fold internal cross-validation indicated that the predictive model incorporating MR radiomics had an average area under the curve (AUC) of 0.834, while the model incorporating CT radiomics had an AUC of 0.787. Conclusions: Model building using quantitative imaging data, blood counts, and blood cytokines resulted in lung inflammation prediction models justifying the study hypothesis. The models yielded very-good-to-excellent AUCs of more than 0.78 on internal cross-validation analyses.

Both incidence and mortality data show that the burden of prostate cancer (PrCa) is greater for African Americans (AA) than for European Americans (EAs), with AAs about 1.7 times more likely to be diagnosed with PrCa than EAs and about 2.4 times more likely to die of this disease. Molecular testing of prostate cancer tissue is increasingly being incorporated into patient management. Current National Comprehensive Cancer Network (NCCN) guidelines recommend three commercially available gene expression based tests for PrCa prognosis: Decipher (GenomeDX), Oncotype GPS (Genomic Health) and Prolaris (Myriad). All three of these tests were developed in predominantly EA patient populations and subsequent studies in AA patients have not included ancestry genotyping to assess genetic admixture. In a pilot study we used the Genomics Resource Information Database (GRID) to compare the research versions of these three commercial gene expression tests for PrCa outcome risk in 157 cancer positive biopsy cores from 55 patients. We also performed ancestry genotyping to estimate each patients European, West African and Native American Ancestry. The overall distribution of scores predicting average, higher than average and lower than average outcome risk differed for the three tests, with higher risk scores in 17%, 7% and 27% and lower risk scores in 69%, 73% and 43% of the cores for the Decipher, GPS and Prolaris tests, respectively. Interestingly, 25%, 37%, and 16% of 56 Cores with high risk pathology (grade group 3-5) were classified as low risk by the Decipher, GPS and Prolaris tests, respectively. In the 21 cores from men with > 70% WA ancestry, 100%, 80% and 30% of cores with intermediate and high risk pathology (grade group 2-5) were paradoxically classified as low risk by the research Decipher, GPS and Prolaris tests, respectively. These preliminary results are consistent with the evidence recently published by Creed et al (2019) suggesting that the currently available commercial genomic tests may underestimate prostate cancer outcome risk in AAs. Ancestry genotyping may be useful in rescaling genomic prostate cancer outcome risk tests that were developed in predominantly EA cohorts for use in AA patients. Reference: Creed JH et al. Cancer Epidemiol Biomarkers Prev, published online 2019. Citation Format: Sandra M. Gaston, Rick A. Kittles, Radka Stoyanova, Teresa M. Giret, Saba A. Ansari, Sanoj Punnen, Alan Pollack. Commercial gene expression tests for prostate cancer outcome risk in men of West African ancestry [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3516.