Citations

Abstract Purpose: Blood-based candidate biomarkers of disease can be monitored by analyzing CTCs and/or circulating cfDNA isolated from the peripheral blood. Our primary objective is to understand the relative contributions of these circulating factors (i.e., CTCs and cfDNA) to the overall disease profile in MBC. Methods: Clinically archived FFPE tumor tissue and prospective blood samples are collected through a minimal risk protocol approved by the Mayo Clinic IRB (#16-001540) from patients with MBC and objective evidence of disease progression. Blood samples include 20 mL whole blood in Streck blood collection tubes (BCTs) for platelet poor plasma (PPP); 20 mL whole blood in AccuCyte BCTs for CTCs, WBCs, and PPP; and 10 mL whole blood in EDTA BCTs for PPP. Nucleated, EpCAM+/cytokeratin+/CD45- CTCs are identified, assessed for ER/HER2 status, and isolated using a centrifugation and direct imaging platform that allows for single cell retrieval (RareCyte). DNA is extracted from PPP, CTCs, WBCs, and FFPE tumor tissue using established methods. Targeted sequencing for SNVs/indels is performed on paired WBCs and CTC-DNA, AccuCyte-cfDNA, Streck-cfDNA, EDTA-cfDNA, and tumor tissue derived DNA using the same NGS panel and informatics pipeline (65 genes; CleanPlex OncoZoom; Paragon Genomics). Results: Tissue and blood samples were collected from 40 patients with metastatic breast cancer. 10 cases were selected for initial analyses on the basis of CTC yield (range 3-113 per 3.75 mL blood); up to 5 CTCs per subject were isolated and pooled for DNA extraction. Plasma cfDNA yields and variant allele frequencies were highly comparable between AccuCyte and Streck collected blood samples. Mutations (range 1-3) were identified in CTC-DNA and/or cfDNA in 9 of 10 cases for a total of 18 detected mutations: 10 in CTC-DNA and cfDNA (BRCA2 N372H, PIK3CA E542K, PIK3CA E545K (x3), PIK3CA H1047R, PTEN R130P, RET G691S, TP53 C135W, TP53 Q192*); 5 in CTC-DNA only (EGFR R521K, EGFR T790M, PIK3CA H1047R, SMAD4 C363Y, TP53 N263D); and 3 in cfDNA only (DNMT3A W893S, DNMT3A S714C, TP53 Q136E). Parallel analyses of samples from 10 more subjects are in progress. Analysis of tumor tissue for all 20 subjects and of EDTA-cfDNA and single CTCs for a subset of cases is ongoing. Updated results will be presented at the meeting. Conclusions: It is feasible to isolate high quality CTCs and cfDNA from the same blood collection tube to perform targeted sequencing; this streamlines specimen processing, decreases overall costs, and minimizes required blood volumes. Importantly, there is overlap in the majority of mutations identified in CTC-DNA and cfDNA, but actionable mutations (e.g., PIK3CA, EGFR) were detected in CTC-DNA only. The clinical and theranostic relevance of these findings is unclear and warrants further investigation. Citation Format: Minetta C. Liu, Karthik V. Giridhar, Roberto A. Leon Ferre, Jamie L. Carroll, Matthew P. Goetz, Tufia C. Haddad, Deanne R. Smith, Siddhartha Yadav, Vidushi Kapoor, Guoying Liu, Tad George, Nolan Ericson, Arturo B. Ramirez, Eric Kaldjian, Keegan E. Haselkorn. Comparison of circulating tumor cell (CTC) derived DNA and circulating cell-free DNA (cfDNA) from simultaneous blood sampling of patients with metastatic breast cancer (MBC) [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 3119.

Abstract Background: Inflammatory breast cancer is a highly aggressive form of breast cancer that robustly forms clusters of tumor emboli in dermal lymphatics and readily metastasizes. Inflammatory breast cancers express high levels of E-cadherin, the major protein of adherens junctions, which may enhance the ability of tumor cells to form such clusters and contribute to metastasis. Seemingly contradictory, E-cadherin has both tumor-suppressing and tumor-promoting roles in cancer; previous studies suggest that this depends on the balance between apical and basolateral cadherin-catenin complexes. Methods: In the present study, we use immunohistochemistry of inflammatory breast cancer patient samples and biochemical analysis of cell lines to determine the expression of PLEKHA7, an apical adherens junction protein. We use viral transduction to ectopically express PLEKHA7 in the SUM149 inflammatory breast cancer cell line. The effect of PLEKHA7 on the aggressiveness of inflammatory breast cancer in 2D, 3D and in-vivo were examined. Results: We determined that PLEKHA7 was deregulated in inflammatory breast cancer, demonstrating improper localization or lost expression in a strong majority of patient samples and very low expression in cell line models. We found that re-expressing PLEKHA7 is sufficient to suppress proliferation, anchorage independent growth, spheroid viability, and tumor growth in-vivo. We also observed a negative-selection pressure within the xenograft tumors to lose PLEKHA7 function or expression.Conclusions: The data indicate that PLEKHA7 is frequently deregulated and acts as a suppressor of inflammatory breast cancer. They also suggest that the resulting imbalance between apical and basolateral cadherin-catenin complexes contributes to growth, survival and emboli-forming capacities of inflammatory breast cancer.

Abstract Objectives: Cognitive computing has the potential to improve efficiency and accuracy of clinical trial enrollment using artificial intelligence. The cognitive computing clinical trial matching (CTM) system used for this study utilizes natural language processing to derive patient and tumor attributes from structured and unstructured electronic medical record (EMR) data. The attributes are matched to complex eligibility criteria in trial protocols. This CTM system has been implemented in our gynecologic oncology practice, for which there is lower representation of ethnic minorities, elderly, and uninsured in National Cancer Institute-sponsored trials. Methods: A clinical research coordinator (CRC) used the CTM system to screen patients for potential clinical trials one day prior to their clinic visit. Trial matches were shared with gynecologic oncology clinicians to raise awareness for study opportunities and assist in treatment decisions. Clinicians were surveyed regarding their experience with the CTM system prepared matches. The identical patients were evaluated by a clinician using the traditional manual screening method. Results: Seventeen patients with new diagnosis, recent resection, or restaging scans were screened for 41 potential gynecologic, phase I, and supportive care clinical trials. Trial screening by the CRC using the CTM system resulted in a total of 119 matched trials (mean: 7 trials/patient) compared to the clinician-generated list of 271 matched trials (mean: 16 trials/patient). The CRC using the CTM system spent an average of 18 minutes/patient (range: 7 to 40 minutes) compared to the clinician average of 22 minutes/patient (range: 7 to 37 minutes). A survey of 8 gynecologic oncology clinicians reported they were willing to spend an average of 8 minutes/patient (range: 1 to 15 minutes) to screen patients for trial eligibility. On independent review, discrepancies occurred when the clinician identified trials that the CRC excluded due to inclusion/exclusion criteria or were unknowingly closed to accrual. In addition, the CRC identified trials that were inadvertently missing from the clinician’s list of active protocols. Consistent with these observations, 100% of surveyed clinicians (8 of 8) agreed that “The CTM system has helped to include and exclude trials prior to discussion with patients.” Conclusions: Use of the CTM system by a CRC was superior to a clinician alone for the accuracy and efficiency of screening clinical trial eligibility. This process improved the identification of trials that a clinician might otherwise miss and the exclusion of trials that were not available or appropriate for patients. The CTM system can reduce the burden of clinicians and research staff to screen patients. Additional research is needed to determine if the process can improve clinical trial enrollment in gynecologic cancers. Citation Format: Thanh P. Ho, Jane M. Helgeson, Angela L. Andring, Jennifer C. Reed, Venessa L. Boyle, Nigar Sofiyeva, Konstantinos Leventakos, Tufia C. Haddad, Andrea E. Wahner Hendrickson, Saravut (John) Weroha. Implementation of cognitive computing to match clinical trials in gynecologic cancers: A single-institution experience [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 01.

ABSTRACT Recent years have witnessed evolution of lung allocation strategies to prioritize sicker recipients. In the pre-transplant period, this has translated into increased utilization of invasive extracorporeal or mechanical ventilatory support as a bridge to lung transplantation. The morbidity associated with these strategies warrants consideration to less invasive respiratory support modalities. Herein, we present a case highlighting successful bridge to lung transplantation with a relatively non-invasive negative pressure ventilator.

Objectives Based on our previous findings that postmenopausal women with estrone (E1) and estradiol (E2) concentrations at or above 1.3 pg/ml and 0.5 pg/ml, respectively, after 6 months of adjuvant anastrozole therapy had a three-fold risk of recurrence, we aimed to identify a single-nucleotide polymorphism (SNP)-based model that would predict elevated E1 and E2 and then validate it in an independent dataset. Patients and methods The test set consisted of 322 women from the M3 study and the validation set consisted of 152 patients from MA.27. All patients were treated with adjuvant anastrozole, had on-anastrozole E1 and E2 concentrations and genome-wide genotyping. Results SNPs were identified from the M3 genome-wide association study. The best model to predict the E1-E2 phenotype with high balanced accuracy was a support vector machine model using clinical factors plus 46 SNPs. We did not have an independent cohort that is similar to the M3 study with clinical, E1-E2 phenotypes and genotype data to test our model. Hence, we chose a nested matched case-control cohort (MA.27 study) for testing. Our E1-E2 model was not validated but we found the MA.27 validation cohort was both clinically and genomically different. Conclusions We identified a SNP-based model that had excellent performance characteristics for predicting the phenotype of elevated E1 and E2 in women treated with anastrozole. This model was not validated in an independent dataset but that dataset was clinically and genomically substantially different. The model will need validation in a prospective study.

Treatment is complex for patients with head and neck (H&N) cancers with specific site of disease, stage, and pathologic findings guiding treatment decision-making. Treatment planning for H&N cancers involves a multidisciplinary team of experts. This article describes supportive care recommendations in the NCCN Guidelines for Head and Neck Cancers, as well as the rationale supporting a new section on imaging recommendations for patients with H&N cancers. This article also describes updates to treatment recommendations for patients with very advanced H&N cancers and salivary gland tumors, specifically systemic therapy recommendations.

2054 Background: Systematic reviews that summarize the toxicity of Immune checkpoint inhibitors (ICIs) become outdated very soon after publication. Therefore, we reported results of a toxicity meta-analysis at 2019 ASCO meeting and informed the intent to create a living systematic review (LSR). LSRs combine human and machine effort and support rapid evidence synthesis and living clinical practice guidelines. Now, we report our experience maintaining a LSR on toxicity of ICIs. Methods: Steps include quarterly literature searches to identify new clinical trials reporting ICI-associated adverse events (AEs), AI-enabled screening of new citations which meet the inclusion criteria, automated cumulative meta-analysis and an online reporting platform. Standard data formats and protocols were designed for inputting text, tables and graphics. Software was written to interpret these data and output the information in the appropriate format, such as a forest plot and summary tables. Finally, a dynamic interface that enables user inputs and displays the associated output was designed. Results: The LSR is continuously updated incorporating toxicity data from new clinical trials as it becomes available. We have screened 8000 relevant citations and summarized the odds of Grade 3 or higher AEs and AEs of special interest in patient receiving ICIs. The results are updated on quarterly basis and are available online. The results are updated on quarterly basis and will be available on a website at the time of publication. Prototype with dummy data is available at this link . This interface can also be manipulated via user input to organize and sort data tables and forest plots by type of cancer, name or mechanism (PD-1 or PD-L1) of ICI agent, single agent or combination, type of control arm, line of treatment and several other clinically relevant filters. For example, a user can instantaneously generate a meta-analysis summarizing the risk of colitis or pneumonitis in metastatic lung cancer trials with pembrolizmuab. Conclusions: This LSR engine can prospectively synthesize toxicity data from ICI trials in an efficient manner providing accurate and timely information for advanced clinical decision support at point-of-care. Efforts are ongoing to improve efficiency of screening, improve AI-enabled processes for automated screening and data abstraction, and test across multiple clinical questions.