Systemic Delivery of mLIGHT-Armed Myxoma Virus Is Therapeutic for Later-Stage Syngeneic Murine Lung Metastatic Osteosarcoma

Cancers that metastasize to the lungs represent a major challenge in both basic and clinical cancer research. Oncolytic viruses are newly emerging options but successful delivery and choice of appropriate therapeutic armings are two critical issues. Using an immunocompetent murine K7M2-luc lung metastases model, the efficacy of MYXV armed with murine LIGHT (TNFSF14/CD258) expressed under virus-specific early/late promoter was tested in an advanced later-stage disease K7M2-luc model. Results in this model show that mLIGHT-armed MYXV, delivered systemically using ex vivo pre-loaded PBMCs as carrier cells, reduced tumor burden and increased median survival time. In vitro, when comparing direct infection of K7M2-luc cancer cells with free MYXV vs. PBMC-loaded virus, vMyx-mLIGHT/PBMCs also demonstrated greater cytotoxic capacity against the K7M2 cancer cell targets. In vivo, systemically delivered vMyx-mLIGHT/PBMCs increased viral reporter transgene expression levels both in the periphery and in lung tumors compared to unarmed MYXV, in a tumor- and transgene-dependent fashion. We conclude that vMyx-mLIGHT, especially when delivered using PBMC carrier cells, represents a new potential therapeutic strategy for solid cancers that metastasize to the lung.

Cancer Diagnosis of Microscopic Biopsy Images Using a Social Spider Optimisation-Tuned Neural Network

One of the most dangerous diseases that threaten people is cancer. If diagnosed in earlier stages, cancer, with its life-threatening consequences, has the possibility of eradication. In addition, accuracy in prediction plays a significant role. Hence, developing a reliable model that contributes much towards the medical community in the early diagnosis of biopsy images with perfect accuracy comes to the forefront. This article aims to develop better predictive models using multivariate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimisation (SSO) algorithm-tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the neural network classifier by the SSO algorithm. The performance of the proposed strategy is analysed with performance metrics such as accuracy, sensitivity, specificity, and MCC measures, and the attained results are 95.9181%, 94.2515%, 97.125%, and 97.68%, respectively, which shows the effectiveness of the proposed method for cancer disease diagnosis.

Cancer Diagnosis of Microscopic Biopsy Images Using Social Spider Optimization Tuned Neural Network

One of the most dangerous diseases that threaten people is Cancer. Cancer if diagnosed in earlier stages can be eradicated with its life threatening consequences. In addition, accuracy in prediction plays a major role. Hence, developing a reliable model that contributes much towards the medical community in early diagnosis of Biopsy images with perfect accuracy come to the scenario. The article aims towards development of better predictive models using multi-variate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimization (SSO) algorithm tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the NN classifier by the SSO algorithm. The performance of the proposed strategy is analysed with the performance metrics, such as accuracy, sensitivity, specificity, and MCC measures, and are attained to be 95.9181%, 94.2515%, 97.125%, and 97.68% respectively, which shows the effectiveness of the proposed method in effective cancer disease diagnosis.

398 neoIRX: a phase II trial of locoregional cytokine therapy to promote immunologic priming and clinical response to neoadjuvant pembrolizumab plus chemotherapy in triple negative breast cancer (TNBC)

BackgroundBackground: The FDA has approved pembrolizumab in combination with neoadjuvant chemotherapy (doxorubicin, cyclophosphamide, paclitaxel [ACT], and carboplatin) for stage II/III TNBC, on the basis of improved event free survival (EFS) and pathologic complete response (pCR) rate in the Keynote-522 study.1 Novel combination immunotherapy strategies may further improve outcomes and allow the opportunity to de-escalate the chemotherapy backbone, potentially mitigating grade III/IV toxicities which occurred in 81% of recipients. We have previously reported safety and feasibility of pre-operative IRX-2, a novel cytokine biotherapeutic, that is administered locoregionally in the peri-areolar tissue to enhance the immune microenvironment within the sentinel lymph nodes, the putative site of antigen presentation.2 In this phase Ib study, pre-operative IRX-2 was safe and was associated with increased tumor infiltrating lymphocytes (sTILs, by H&E and multispectral immunofluorescence [mIF]), PD-L1 expression (Ventana SP142 assay, mIF), and lymphocyte activation (by RNA sequencing). Similar effects were observed in a pre-operative head and neck carcinoma trial. These findings support further study of IRX-2 in combination with anti-PD-1 in early stage TNBC.MethodsMethodsneoIRX is an open-label, phase II trial to evaluate the clinical and immunological activity of induction IRX-2 plus pembrolizumab, followed by de-escalated chemotherapy (ACT) and pembrolizumab as neoadjuvant therapy in TNBC. Patients are randomized to receive pembrolizumab induction (single dose 200mg IV, n=15), versus pembrolizumab + IRX-2 induction (1mL SQ x 2 daily, x 10 days, n=15), followed by research biopsy. All patients will then receive neoadjuvant pembrolizumab plus ACT every three 3 weeks. Eligible subjects will have previously untreated, resectable stage II/III TNBC. The primary endpoint is pCR. The secondary endpoint is safety. On-treatment biopsies will permit a prospective, randomized validation of previously reported immunomodulatory effects of IRX-2 (sTILs, PD-L1, lymphocyte RNA signatures). As of 7/28/2021, n=7/30 subjects are enrolled (Providence Cancer Institute, Portland, OR, Providence St. John’s Cancer Institute, Santa Monica, CA, Baylor Medicine, Houston, TX).Trial RegistrationNCT04373031ReferencesSchmid PN. Engl J Med 2020; 382:810–821.Page DB. Clinical Cancer Research 26.7(2020):1595–1605.Ethics ApprovalThe study protocol was approved by the Providence Portland Medical Center IRB committee and was conducted in accordance with the ethical standards established by the Declaration of Helsinki, PH&S IRB# 2019000486. Written informed consent was obtained for all trial participants.

Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms

Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.

Metabolic Tumor Microenvironment Characterization of Contrast Enhancing Brain Tumors Using Physiologic MRI

The tumor microenvironment is a critical regulator of cancer development and progression as well as treatment response and resistance in brain neoplasms. The available techniques for investigation, however, are not well suited for noninvasive in vivo characterization in humans. A total of 120 patients (59 females; 61 males) with newly diagnosed contrast-enhancing brain tumors (64 glioblastoma, 20 brain metastases, 15 primary central nervous system (CNS) lymphomas (PCNSLs), and 21 meningiomas) were examined with a previously established physiological MRI protocol including quantitative blood-oxygen-level-dependent imaging and vascular architecture mapping. Six MRI biomarker maps for oxygen metabolism and neovascularization were fused for classification of five different tumor microenvironments: glycolysis, oxidative phosphorylation (OxPhos), hypoxia with/without neovascularization, and necrosis. Glioblastoma showed the highest metabolic heterogeneity followed by brain metastasis with a glycolysis-to-OxPhos ratio of approximately 2:1 in both tumor entities. In addition, glioblastoma revealed a significant higher percentage of hypoxia (24%) compared to all three other brain tumor entities: brain metastasis (7%; p < 0.001), PCNSL (8%; p = 0.001), and meningioma (8%; p = 0.003). A more aggressive biological brain tumor behavior was associated with a higher percentage of hypoxia and necrosis and a lower percentage of remaining vital tumor tissue and aerobic glycolysis. The proportion of oxidative phosphorylation, however, was rather similar (17–26%) for all four brain tumor entities. Tumor microenvironment (TME) mapping provides insights into neurobiological differences of contrast-enhancing brain tumors and deserves further clinical cancer research attention. Although there is a long roadmap ahead, TME mapping may become useful in order to develop new diagnostic and therapeutic approaches.

Nanochips assisted peptide screening for clinical development of CAR-T cell immunotherapy

Immunotherapy using CAR-T cells is a new paradigm technology for cancer treatment. To avoid severe side effects and tumor escape variants observed for conventional CAR-T cells approach, adaptor CAR technologies are under development, where intermediate target modules redirect immune cells against cancer. In this work, silicon nanowire field effect transistors are used to assist in the development of target modules for an optimized CAR-T cell operation. Focusing on a library of seven variants of E5B9 peptide that is used as CAR peptide epitope, we performed multiplexed binding tests in serum using nanosensor chips. Peptides have been immobilized onto the sensor to compare the signals of transistor upon titration with anti-E5B9 antibodies. Correlation analysis of binding affinities and sensitivities enabled a selection of best candidates for the interaction between CAR and target modules. Finally, cytotoxic functionality of CAR-T cells in combination with the selected target modules were successfully proven. Our results open the perspective for the nanobiosensorics to go beyond the early diagnostics in the field of clinical cancer research, and paves the way towards personalization and efficient monitoring of the immunotherapeutic treatment, where the quantitative analysis with the standard techniques is not an option.

NCI’s national treatment trial networks: Experience and adaptations during COVID-19.

1562 Background: The National Cancer Institute supports several national trial networks which responded rapidly to the COVID-19 pandemic to overcome operational barriers to clinical cancer research. The National Clinical Trials Network (NCTN) focuses on late phase treatment trials, while the Experimental Therapeutics Clinical Trials Network (ETCTN) conducts early phase treatment trials. We report findings on the experience and adaptations of these networks during COVID-19. Methods: Using 2019 and 2020 accrual data, we analyzed changes in accrual levels and demographics. We also evaluated changes in trial activation numbers and timelines. In July 2020, we surveyed 255 investigators from academic and community sites to assess changes in research practices and get feedback on modified processes implemented by NCI to address trial conduct during the pandemic. Results: Accrual across the NCTN and ETCTN fell significantly in mid-March 2020, dropping from a weekly average of 307 patients in February to 169 the week of March 23-29. Accrual began to recover in June and July but did not return to pre-pandemic levels until September. Accrual in November and December 2020 followed the patterns seen in 2019, with short-term drops around major holidays. Non-White participants were enrolled to NCTN and ETCTN trials at similar monthly rates throughout 2019 and 2020, with slightly higher overall enrollment in 2020 (23.7% vs. 22.7%). New trials continued to be developed and activated throughout 2020. Between 2017 and 2019, an average of 71 trials were activated per year (NCTN = 46, ETCTN = 25), compared to 84 activated in 2020 (NCTN = 58, ETCTN = 26). The average time to trial activation was similar or slightly longer in 2020 compared to 2019. The investigator survey yielded 111 responses (43.5% response rate). 43% of respondents’ sites paused enrollment to phase 1 trials during the pandemic, compared to 18% for phase 3 trials. Many sites temporarily stopped opening new trials and processing specimens. Sites were more likely to keep enrolling to trials offering clear potential benefit and pause complex trials that required more patient contact. Respondents attributed some of the decline in accrual to a reduction in overall patient volume, increased patient concerns, and reduced research staff on site. Respondents were asked to rate the usefulness of modified trial processes NCI put in place during the pandemic. Telehealth was rated most useful (avg. 4.6/5), followed by shipping oral IND agents to enrolled patients (4.5/5), remote informed consent (4.2/5), coordinating care with local providers (3.9/5), and remote auditing (3.7/5). Conclusions: The cancer trials community has an opportunity to learn from working through the challenges of COVID-19. NCI will seek to continue and expand on modifications to clinical trial processes that have the potential to improve operational efficiency, reduce cost, and help bring trials to more patients.