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Esophageal cancer (EC) has one of the highest mortality rates among cancers, making it imperative that therapies are optimized and dynamically adapted to individuals. In this regard, liquid biopsy is an increasingly important method for residual disease monitoring. However, conflicting detection rates (14% versus 60%) and varying cell-free circulating tumor DNA (ctDNA) levels (0.07% versus 0.5%) have been observed in previous studies. Here, we aim to resolve this discrepancy. For 19 EC patients, a complete set of cell-free DNA (cfDNA), formalin-fixed paraffin-embedded tumor tissue (TT) DNA and leukocyte DNA was sequenced (139 libraries). cfDNA was examined in biological duplicates and/or longitudinally, and TT DNA was examined in technical duplicates. In baseline cfDNA, mutations were detected in 12 out of 19 patients (63%); the median ctDNA level was 0.4%. Longitudinal ctDNA changes were consistent with clinical presentation. Considerable mutational diversity was observed in TT, with fewer mutations in cfDNA. The most recurrently mutated genes in TT were TP53, SMAD4, TSHZ3, and SETBP1, with SETBP1 being reported for the first time. ctDNA in blood can be used for therapy monitoring of EC patients. However, a combination of solid and liquid samples should be used to help guide individualized EC therapy.

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Correlation of Affymetrix microarray expression data and IHC scoring of three genes in independent TNBC dataset

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Purpose: Multiple myeloma is an incurable malignant plasma cell disease characterized by survival ranging from several months to more than 15 years. Assessment of risk and underlying molecular heterogeneity can be excellently done by gene expression profiling (GEP), but its way into clinical routine is hampered by the lack of an appropriate reporting tool and the integration with other prognostic factors into a single “meta” risk stratification.Experimental Design: The GEP-report (GEP-R) was built as an open-source software developed in R for gene expression reporting in clinical practice using Affymetrix microarrays. GEP-R processes new samples by applying a documentation-by-value strategy to the raw data to be able to assign thresholds and grouping algorithms defined on a reference cohort of 262 patients with multiple myeloma. Furthermore, we integrated expression-based and conventional prognostic factors within one risk stratification (HM-metascore).Results: The GEP-R comprises (i) quality control, (ii) sample identity control, (iii) biologic classification, (iv) risk stratification, and (v) assessment of target genes. The resulting HM-metascore is defined as the sum over the weighted factors gene expression–based risk-assessment (UAMS-, IFM-score), proliferation, International Staging System (ISS) stage, t(4;14), and expression of prognostic target genes (AURKA, IGF1R) for which clinical grade inhibitors exist. The HM-score delineates three significantly different groups of 13.1%, 72.1%, and 14.7% of patients with a 6-year survival rate of 89.3%, 60.6%, and 18.6%, respectively.Conclusion: GEP reporting allows prospective assessment of risk and target gene expression and integration of current prognostic factors in clinical routine, being customizable about novel parameters or other cancer entities. Clin Cancer Res; 17(23); 7240–7. ©2011 AACR.

Background Tumor complexity and heterogeneity require matching of actionable genomic alterations with available therapy to increase response rates and prolong survival in patients. However, it has been a long-standing challenge for treating oncologists to select an effective patient-specific therapeutic strategy due to the molecular rationale, disease relevance, and patient-specific issues. To provide a decision-support tool to aid oncologists and educate patients, we have developed a set of molecular and clinical evidence-based criteria as an algorithm for ranking therapeutic strategies in order to deliver optimal care and improve outcomes in patients with malignancy. Methods History of present illness (HPI) and comprehensive genomic profiling (CGP) results of 203 patients with malignant tumors were reviewed by our multidisciplinary Molecular Tumor Board (MTB) from June 2021 to August 2022, and therapeutic recommendations were provided with Matching Score based on molecular matching only as well as with Ranking Score calculated by our molecular and clinical evidence-based algorithm with the criteria not only focusing on molecular matching, but also including disease relevance, patient-specific clinical considerations and treatment availability as weighting factors (Cohort 2). The other 50 patients with previously treated solid tumors reviewed by MTB (before 2018) and treatment recommendations provided with Matching Score only, were used as a control group (Cohort 1). The matching rates from recommendations and treatment outcomes of the patients were then assessed. Results In Cohort 1, of the 50 patients, 33 patients (66%) received matched therapeutic plans recommended by our MTB. The other 17 patients were not on a matched plan from the MTB. Twelve patients (36.4% of 33 patients) achieved progression-free survival (PFS) at 12-week time point, including 8 patients (24.2%) achieved PFS at 6-month time point. The other 21 patients (63.6% of 33 patients) could not be assessed due to treatment termination per drug toxicity or death before the follow-up time point. The median PFS/overall survival (OS) for patients with a Matching Score > 50% (N = 17) were 7.5/10.5 months, whereas with Matching Score ≤ 50% (N = 16) were 3/7.35 months. In Cohort2, of the 203 patients, 89 (43.8%) patients have initiated matched therapeutic plans and follow-up is ongoing. Updated results will be presented at the meeting. Conclusion Our novel molecular and clinical evidence-based algorithm may be used to support oncologists decision-making to utilize the most clinically appropriate and effective therapeutic options. Further validation studies and development of an user-friendly computational ranking platform based on the algorithm are planned in order. Citation Format: Yuliang Sun, Tobias Meissner, Rachel Elsey, Leah Theisen, Crystal Hattum, Bing Xu, John Lee, Casey Williams. Development/validation of a molecular and clinical evidence-based algorithm for selecting optimal precision therapeutic strategy for cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1057.

This study evaluated the effect of experimental solutions containing plant extracts on bacterial species and enamel caries prevention. Microcosm biofilm was produced from human saliva mixed with McBain saliva (0.2% sucrose) on bovine enamel for 5 days (3 days under anaerobiosis and 2 days under aerobiosis) at 37°C. From the 2nd day, the following treatments were applied (1 × 60 s/day): Vochysia tucanorum (10 mg/mL); Myrcia bella (5 mg/mL); Matricaria chamomilla (80 mg/mL); Malva sylvestris, fluoride, and xylitol (Malvatricin Plus®); 0.12% chlorhexidine (CHX, PerioGard®); and PBS (negative control). The medium pH was measured. Quantitative polymerase chain reaction was performed for the detection of Streptococcus mutans and Lactobacillus spp. Enamel demineralization was measured by spectral-domain optical coherence tomography. The data were compared by means of the Kruskal-Wallis/Dunn, two-way ANOVA/Bonferroni, and ANOVA/Tukey tests (p < 0.05). The pH decreased after sucrose exposure; only CHX reestablished pH >5.5 by the last day. CHX also eliminated Lactobacillusspp., but the other treatments did not differ significantly from PBS. Malvatricin Plus® and CHX eliminated S. mutans, but the other treatments did not differ from PBS. Similar results were seen concerning the reduction of lesion depth and reflectivity. The experimental natural-extract solutions were ineffective against cariogenic bacteria and in preventing the development of enamel caries.

Background Cancer is a somatic evolutionary disease and adenocarcinomas of the stomach and gastroesophageal junction (GC) may serve as a two-dimensional model of cancer expansion, in which tumor subclones are not evenly mixed during tumor progression but rather spatially separated and diversified. We hypothesize that precision medicine efforts are compromised when clinical decisions are based on a single-sample analysis, which ignores the mechanisms of cancer evolution and resulting intratumoral heterogeneity. Using multiregional whole-exome sequencing, we investigated the effect of somatic evolution on intratumoral heterogeneity aiming to shed light on the evolutionary biology of GC. Methods The study comprised a prospective discovery cohort of 9 and a validation cohort of 463 GCs. Multiregional whole-exome sequencing was performed using samples form 45 primary tumors and 3 lymph node metastases (range 3–10 tumor samples/patient) of the discovery cohort. Results In total, the discovery cohort harbored 16,537 non-synonymous mutations. Intratumoral heterogeneity of somatic mutations and copy number variants were present in all tumors of the discovery cohort. Of the non-synonymous mutations, 53–91% were not present in each patient’s sample; 399 genes harbored 2–4 different non-synonymous mutations in the same patient; 175 genes showed copy number variations, the majority being heterogeneous, including CD274 (PD-L1). Multi-sample tree-based analyses provided evidence for branched evolution being most complex in a microsatellite instable GC. The analysis of the mode of evolution showed a high degree of heterogeneity in deviation from neutrality within each tumor. We found evidence of parallel evolution and evolutionary trajectories: different mutations of SMAD4 aligned with different subclones and were found only in TP53 mutant GCs. Conclusions Neutral and non-neutral somatic evolution shape the mutational landscape in GC along its lateral expansions. It leads to complex spatial intratumoral heterogeneity, where lymph node metastases may stem from different areas of the primary tumor, synchronously. Our findings may have profound effects on future patient management. They illustrate the risk of mis-interpreting tumor genetics based on single-sample analysis and open new avenues for an evolutionary classification of GC, i.e., the discovery of distinct evolutionary trajectories which can be utilized for precision medicine.

Background: We have previously shown that expression of the IL8/VEGFA-metagene eliminates the good prognostic effect of TILs in TNBC (PMID 21978456, 28750120). We also showed that the VEGFA metagene predicted response to neoadjuvant bevacizumab in the GeparQuinto trial (Karn 2017 SABCS #851166). The main cellular sources of the transcripts that comprise the VEGFA metagene are unknown since mRNA profiling of bulk biopsies contains signals from different cell types. Methods: Individual genes that comprise the VEGFA metagene were measured in bulk tissue- and single cell-RNA-Seq from breast cancer subtypes and normal cells on different platforms (Affymetrix n=4915, Agilent n=597, Illumina n=2433, RNA-Seq n=1215, Exome Capture RNA-Seq n=226, HTG-Seq n=243, sc-RNA-Seq n=24710). For blinded, orthogonal validation we performed immunohistochemistry. Effect of neoadjuvant chemotherapy with or without bevacizumab was studied by RNA-Seq and IHC on samples from GeparQuinto trial. SWOG S0800 (GSE114403), PROMIX (GSE87455), and GeparSixto trials were used for validation. TCGA was mined for mutations and somatic CNA. RNA-Seq from GeparNuevo was used for correlation with checkpoint inhibitor treatment. Results: We identified a stable core of six genes (VEGFA, ANGPTL4, ADM, NDRG1, DDIT4, CSTB) in different cohorts. Strong expression of this signature was mainly restricted to TNBC subtype and associated with poor prognosis within this subgroup. Single cell RNA-Seq of breast epithelial cells from 4 reduction mammoplasties and 4 TNBC revealed that these genes are coexpressed in individual epithelial cells and not associated with endothelial cells. In line with their presumed functions in cellular stress and hypoxia, immunohistochemistry revealed strong para-necrotic expression in TNBC. Moreover, high gene expression in TNBC was associated with mutations in DNA damage control pathways, somatic copy number alterations, and lower TILs. While chemotherapy led to downregulation, bevacizumab increased expression. In multivariate analysis, high pretreatment values predict pCR to both bevacizumab and chemotherapy (OR 2.40, P=0.006), which may be explained by sensitivity of tumors which are already under cellular stress. On the other hand, expression of the VEGFA metagene seems to create an immunosuppressive environment that counteracts the positive prognostic effect of TILs. In pre-treatment biopsies from the GeparNuevo checkpoint inhibitor trial we found a negative correlation of VEGFA metagene expression with the amount of the recently identified tissue-resident memory T cell subset (CD8TRM, PMID 29942092; P=0.002), while the subsequent increase of CD8TRM during treatment was larger in tumors with high VEGFA (P=0.019). Conclusions: Perinecrotic carcinoma cells under stress from hypoxia and or chromosomal instability are the source of the VEGFA metagene signature. Its predictive value in TNBC suggests estimating and reporting the amount of necrosis in the pathology report may be helpful in predicting response to preoperative chemotherapy, and could be used as stratification factor in clinical trials. The signature indicates an immunosuppressive environment and should be further studied in the context of immune therapies in combinations with anti-angiogenic treatment. Citation Format: Karn T, Denkert C, Sinn BV, Weber K, Nekljudova V, Rody A, Meissner T, Hatzis C, El-Balat A, Becker S, Solbach C, Untch M, Schneeweiss A, von Minckwitz G, Loibl S, Pusztai L, Holtrich U. Single-cell profiling identifies hypoxic carcinoma cells as source of an immunosuppressive VEGFA metagene [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-10-01.

Background: Tumor complexity and heterogeneity require matching of actionable genomic alterations with available therapy to increase response rates and prolong survival in patients with cancer. It has been a long-standing challenge for treating oncologists to select an effective patient-specific therapeutic strategy due to the molecular rationale, disease relevance, patient-specific issues, including patient access to treatments. We have developed a set of molecular and clinical evidence-based criteria as an algorithm for ranking therapeutic strategies in order to deliver optimal care and improve outcomes in patients with metastatic malignancies. Methods: Patients included in the analysis received comprehensive genomic profiling (CGP) for selected biomarkers. CGP results of 50 patients with stage IV tumors were reviewed by our multidisciplinary Molecular Tumor Board (MTB), after which treatment recommendations were generated based on molecular matching only (Cohort 1). CGP results of the other current 29 patients with stage IV breast cancer were reviewed by MTB, and therapeutic recommendations were provided with ranking scores calculated by our molecular and clinical evidence-based algorithm with the criteria not only focusing on molecular matching, but also including disease relevance, patient-specific clinical considerations and treatment availability as weighting factors (Cohort 2). The matching rates from recommendations and treatment outcomes of the two cohorts were then assessed. Results: In Cohort 1, of the 50 patients, 34 patients (68%) were initiated with personalized therapeutic plans based on molecular matching that were recommended by our MTB. The other 16 patients were not treated with molecular matching therapeutic plans recommended by MTB, as the results of treating oncologists’ choices, treatment availability or patient preference. Patients were followed and outcomes were then evaluated. Thirteen patients (38.2% of 34 patients) achieved progression-free survival (PFS) at 12-week time point, and 6 patients achieved PFS (including stable disease, N=4 and partial response, N=2) at 6-month time point. The other 21 patients (61.8% of 34 patients) could not be assessed due to disease progression, treatment termination per drug toxicity or death before the follow-up time point. Cohort 2 is ongoing, the results of which will be presented in the meeting. Conclusion: Our novel molecular and clinical evidence-based algorithm may be used to support oncologists decision-making to utilize the most clinically appropriate and effective therapeutic options. Additional studies with larger sample sizes in different type of solid tumors are planned in order to determine a clinically and statistically relevant range of score for the treatment recommendations with optimal clinical outcomes. Citation Format: Yuliang Sun, Tobias Meissner, Rachel Elsey, Leah Theisen, Jason Jones, Casey Williams. Development of a molecular and clinical evidence-based algorithm for selecting optimal precision therapeutic strategy for patients with metastatic breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4095.

​Introduction: Analysis of whole transcriptomics datasets to quantify gene expression and determine differentially expressed genes has provided valuable information for precision medicine. Evaluating enrichment of specific gene groups and pathways, and visualizing these results is an important step to obtain inferences about patient treatment outcomes, overall survival, drug resistance and therapeutic targets. However, pathway analysis of cancer patient samples in a N-of-1 setting without their respective control (normal) samples remains to be challenging. There is a crucial need for methods and tools to conduct and visualize pathway analysis results in this setting. Methods and Results: Our method focuses on a visualization technique showing patient-specific pathway activation relative to reference populations from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEX). Patient-specific pathway activity scores are displayed within the respective percentiles and averages of the TCGA and GTEX cohorts to visualize a patient's individual pathway activation profile within the TCGA tumor and GTEX normal reference population. Patient-specific pathway scores are derived from an individual patient's transcriptomic profile and transformed into activity scores for each of 14 cancer pathways. This allows for the comparison of an individual patient’s pathway activation profile with reference samples from TCGA and GTEX. We used PROGENy (Pathway RespOnsive GENes for activity inference) to calculate activity scores for the 14 cancer related pathways. The pathway activity scores of each new tumor sample are scaled using the parameters of scaled pathway activity scores obtained from TCGA and GTEX gene expression data of all cancer tissue types. Finally we visualized the pathway activity scores of each patient sample. Conclusion: Our visualization method is expected to unravel the activation patterns of each of the 14 cancer pathways in our patient samples. Specific pathway genes can then be evaluated in the patient samples to identify causal mutations and their associations with the pathway activity scores hence aid in treatment selection and in the further development of precision medicine therapeutic solutions. Citation Format: Padmapriya Swaminathan, Casey B. Williams, Tobias Meissner. Patient-specific visualization of cancer pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2271.

Introduction: The adoption of routine tumor sequencing, and the incorporation of biomarker-based clinical trials in the treatment of cancer has been steadily rising over the past few years. Access and ability to participate in clinical trials is a key element in providing therapeutic options for cancer patients.At the Avera Cancer Institute, options of biomarker-based clinical trials are routinely presented to the treating clinicians at the molecular tumor board (MTB). With everincreasing numbers of trials, a systematic method for keepingtrack of available trials andevaluating patients for trial options was needed. A search for solutions led to only one open-source tool, MatchMiner, but it did not meet our needs. Methods and Results: We developed an open-source web application, TrialMatch, that has the ability to (i) add trials through a semi-automated curation interface, (ii) browse and search trials, (iii) and match patients to biomarker-based trials.TrialMatch is an R Shiny application that uses a mongo database to store the trial data. Software installation and application instantiation is managed through Docker. Curate This section has fields for a user to enter trial name and a NCT ID from clinicaltrials.gov. It then queries the clinicaltrials.gov API and fetches data about the sponsor, treatment, etc.Although disease and biomarker data are available on clinicaltrials.gov, they are not stored as discrete fields that can be queried, and hence require manual input. The user provides disease type based on the OncoTree classification, as well as biomarker information for mutations, copy numbers, fusions, TMB, MSI status, PD-L1 status, RNA expression, and disease specific markers such as ER/PR/HER2 status.All information for a particular trial is entered in a mongo database collection, with each trial stored as a document. The ability to add and edit data fields can be restricted to specific users. Browse This section allows the user to browse through all trials with key information such as NCT ID, trial name, disease, and biomarkers. Each row represents one trial and can be expanded to show more detailed information. This is useful to keep track of trials, and to quickly filter trials to a specific disease or biomarker. Match This section displays a patient-centric view of the trials that each patients’ age, sex, disease, and biomarkers have matched to. The matching can be automated for scheduled recurrentruns. At Avera, this is used during MTB to evaluate treatment options, but can also be used by clinicians to get a sense of the trials available for their patients, while awaiting formal review. Conclusion: We believe that this computational tool fulfills a crucial need in the clinical application of precision oncology, and will ultimately increase clinical trial enrollment for patients. Citation Format: Anu Amallraja, Shivani Kapadia, Padmadpriya Swaminathan, Casey Williams, Tobias Meissner. TrialMatch: A computational resource for the management of biomarker-based clinical trials at a precision oncology center [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1899.

e13545 Background: The ASCO Quality Oncology Practice Initiative (QOPI) was established in 2006 to facilitate quality measurement and continuous improvement of cancer care.1 ASCO and QOPI leadership encourage a continuous learning environment to develop new tools and methodologies that improve quality and maximize outcomes and value in oncology practice.2 Parallel advances in tumor molecular profiling, bioinformatics, and EMR data curation are adding new insights to support the real-world research of longitudinal patient journeys. Methods: Referencing the QOPI Certification Track 2021 Measures Summary, we employed data visualization techniques to create a precision oncology dashboard for breast cancer. This evaluation included 409 breast cancer patients for whom genomic testing was performed and presented at the Avera molecular tumor board. Patients were then matched to appropriate biomarker-based targeted therapies. Results: The dashboard identified and prioritized areas for enhanced data capture to inform strategies for care-coordination, research, and delivery of specialized care. Conclusions: An interactive, visual platform may help facilitate the integration of QOPI reporting into CancerLinQ, thereby fostering a real-time, bidirectional system for quality reporting, clinical decision support, and rapid learning.3,4 The next stage will (1) expand the tumor list; and (2) apply NLP methods to assess accuracy, outcomes and temporal reasoning to create interactive graphs for patient referral patterns and surgery decision drivers. References: 1. Neuss MN, Desch CE, McNiff KK, et al: A process for measuring the quality of cancer care: The Quality Oncology Practice Initiative. J Clin Oncol. 2005 2. Blayney DW, McNiff K, Eisenberg PD, Gilmore T, Jacobsen PB, Jacobson JO, Kadlubek PJ, Neuss MN, Simone J. Development and future of the American Society of Clinical Oncology's Quality Oncology Practice Initiative. J Clin Oncol. 2014 3. Blayney DW: Enhancing quality through innovation: American Society of Clinical Oncology presidential address 2010. J Clin Oncol. 2010 4. Schilsky R, Hauser R, Mann J, et al: Lessons learned from the development of the CancerLinQ prototype: Clinical decision support. J Clin Oncol. 31, 2013[Table: see text]

TPS6605 Background: Precision medicine represents a new era in oncology and population health. Comprehensive molecular profiling and hereditary cancer testing are increasingly important in the routine management of many malignancies, yet may not be widely adopted in many practices and leads to patients not consistently receiving care recommended by current national guidelines such as NCCN and ASCO. In addition, available targeted DNA sequencing panels may be limited to only defined drivers and lack detection in precancerous conditions. Whole exome sequencing (WES) combined with whole transcriptome sequencing (WTS) represent an expanded testing approach that identifies additional genetic changes beyond standard commercial panel tests. Current evidence suggests that WES/WTS is feasible to carry out in routine clinical practice, with improved detection of somatic alterations compared to smaller panels, and the promise to enhance access to targeted therapies and clinical trials. The primary purpose of the ASAP study is to understand the breadth of molecular characteristics present in participants cared for in a large integrated community-based health system, by linking comprehensive molecular profiles with clinical data extracted and curated from the electronic medical record. Methods: Avera Health is a large, Midwestern, predominantly rural vertically integrated health system with six regional cancer centers and over 40 oncology outreach sites across South Dakota and surrounding states. The ASAP study was initiated in November 2021 in GYN and as of February, the study is enrolling in the Otolaryngology clinic and expected to open in medical oncology and hematology in March/April 2022. The study will expand to include additional cancer centers outside of Sioux Falls later in 2022 and is anticipated to be open for accrual for 5 years. Enrolled participants are expected to receive WES/WTS and proteomics on their primary tumor and metastatic sites were feasible as well as hereditary testing, pharmacogenomics (PGx) and microbiome profiling. Liquid biopsies will be performed every 3 months while on treatment or when treatment is changed, in follow up to coincide with scheduled radiographic testing, and as part of annual routine surveillance. For participants not diagnosed with cancer, each participant will receive hereditary testing, PGx, microbiome, and a liquid biopsy at least annually. Participants also consent to sharing their electronic health information. Future stages of this work will employ natural language processing (NLP) methods to develop algorithms to help identify opportunities to improve patient outcomes. Projected enrollment is 1000 participants in year 1, 1500 participants in year 2, and up to 3000 annually thereafter. Approximately 50 patients have consented as of February 1 2022. Clinical trial information: NCT05142033.

e15036 Background: Advances in tumor molecular profiling and bioinformatics are adding new insights to support real-world research. In this study, paired tumor NGS testing data was curated and analyzed from electronic medical records (EMR) to understand mutational and targeted therapy landscape. Methods: A database initiated in 2013 to collect data, including outcomes, on all patients referred to the molecular tumor board at the Avera Cancer Institute was curated and analyzed. A data pipeline that combined results from curated NGS data with curated EMR data from Avera was used to correlate targetable mutations to a corresponding therapy and assessed treatment patterns. Results: Data were from 1804 patients with 44 cancer types including breast (462; 26%), lung (338;19%), colorectal (198;11%), ovarian (168;9%), and uterine (100;5%). Overall, 83% of patients had a positive NGS result and 67% had a targetable mutation per NCCN guidelines. 32% of patients with a targetable mutation received targeted therapy per guideline. Overall, 381 patients with 28 tumor types received targeted therapies based on NGS results and targetable biomarkers. Of these, 288/381 (76%) were stage III-IV and 93/381 (24%) early stage/stage unknown. In non-small cell lung cancer, 76% of stage III-IV patients with mutations in EGFR (exon 19 deletion, L858, L861, S768, G719), MET (exon 14 skipping, amplification), BRAF V600 and ALK/ ROS1/ RET fusions received a targeted therapy per NCCN guidelines. In advanced breast cancer, 97% with ERBB2 amplification received targeted therapy (trastuzumab, pertuzumab, neratinib, lapatinib, tucatinib). 8% of advanced breast cancer patients with PIK3CA mutation received alpelisib, with all patients receiving the drug since 2019. 38% advanced breast cancer patients with BRCA1/2 mutation received Olaparib. In ovarian cancer, 48% of advanced stage patients received targeted therapy for BRCA 1/2, ATM, CDK12, CHEK2, ERBB2, FANCL, KRAS, ALK, BRAF alterations. Olaparib was used in 70% of advanced prostate cancer patients with BRCA1/2 mutations. Dabrafenib combined with Trametinib were most preferred for BRAF V600 mutation in melanoma and colorectal tumors respectively. Off label use of targeted therapy was observed in about 6% of advanced patients; examples include trametinib for KRAS/NRAS mutation [non-small cell lung cancer, colorectal, pancreatic, ovarian], PARP inhibitors for BRCA1/2, ATM mutations [urothelial, peritoneal], anti-ERBB2 inhibitors for ERBB2 in-frame, missense, exon 20 insertion mutations [lung and breast]. Conclusions: These study results demonstrate that automated data mining can provide insights into the genomic and therapeutic landscape of a real-world patient population. Across all tumor types, most patients are being given appropriate targeted therapies as per guidelines. Off-label use of targeted therapy was also observed.

In dental research, the morphometric assessment of restorations is a challenge. This also applies to the assessment of the length of interfacial adhesive defects in composite restorations as a measure of tooth-restoration bond failure. The determined mean fractions of interfacial gap length on enamel and dentin interfaces deviate from the true means (N → ∞), depending on the number (Ni) of object layers assessed. Cervical composite restorations were imaged with spectral domain optical coherence tomography (SD-OCT). The mean fractions of interfacial gap length on enamel and dentin were determined for an increasing number of OCT cross-sectional images (B-scans) per restoration and were graphically displayed as a function of the number of B-scans. As the number of B-scans increased, the calculated object means approached a range of ±2.5%. This analysis is appropriate for displaying the relationship between the determined mean fraction of interfacial gap length at the enamel/dentin-restoration interface and the number of B-scans.

This study evaluated the effect of experimental solutions containing plant extracts on bacterial species and enamel caries prevention. Microcosm biofilm was produced from human saliva mixed with McBain saliva (0.2% sucrose) on bovine enamel for 5 days (3 days under anaerobiosis and 2 days under aerobiosis) at 37°C. From the 2nd day, the following treatments were applied (1 × 60 s/day): Vochysia tucanorum (10 mg/mL); Myrcia bella (5 mg/mL); Matricaria chamomilla (80 mg/mL); Malva sylvestris, fluoride, and xylitol (Malvatricin Plus®); 0.12% chlorhexidine (CHX, PerioGard®); and PBS (negative control). The medium pH was measured. Quantitative polymerase chain reaction was performed for the detection of Streptococcus mutans and Lactobacillus spp. Enamel demineralization was measured by spectral-domain optical coherence tomography. The data were compared by means of the Kruskal-Wallis/Dunn, two-way ANOVA/Bonferroni, and ANOVA/Tukey tests (p0.05). The pH decreased after sucrose exposure; only CHX reestablished pH5.5 by the last day. CHX also eliminated Lactobacillusspp., but the other treatments did not differ significantly from PBS. Malvatricin Plus® and CHX eliminated S. mutans, but the other treatments did not differ from PBS. Similar results were seen concerning the reduction of lesion depth and reflectivity. The experimental natural-extract solutions were ineffective against cariogenic bacteria and in preventing the development of enamel caries.

Das ultraschnelle Laden mit Gleichspannung oder englisch High Power Charging (HPC) stellt eine bisher unerreichte Spitzenbelastung fur das elektrische System eines Fahrzeugs dar. Vor allem Steckverbinder erfahren durch diese Beanspruchung eine hohe Kontakterwarmung und damit einhergehend einen erhohten Verschleis. Die thermischen Engpasse liegen in der Regel in den Kontaktelementen und Kontaktwiderstanden, also an unzuganglichen Positionen. Dadurch konnen reale Temperatursensoren die Schwachstellen gar nicht oder nur unter hohem Aufwand direkt uberwachen. Die systemische Warmesimulation erlaubt „virtuelle“ Temperatursensoren, deren Messgenauigkeit, Zuverlassigkeit, Robustheit und Sicherheit mit „echten“ Sensoren kaum erreicht werden konnen.

Personalized treatment vs. standard of care is much debated, especially in clinical practice. Here we investigated whether overall survival differences in metastatic colorectal cancer patients are explained by tumor mutation profiles or by treatment differences in real clinical practice. Our retrospective study of metastatic colorectal cancer patients of confirmed European ancestry comprised 54 Americans and 54 gender-matched Germans. The Americans received standard of care, and on treatment failure, 35 patients received individualized treatments. The German patients received standard of care only. Tumor mutations, tumor mutation burden and microsatellite status were identified by using the FoundationOne assay or the IDT Pan-Cancer assay. High-risk patients were identified according to the mutational classification by Schell and colleagues. Results: Kaplan&ndash, Meier estimates show the high-risk patients to survive 16 months longer under individualized treatments than those under only standard of care, in the median (p &lt, 0.001). Tumor mutation profiles stratify patients by risk groups but not by country. Conclusions: High-risk patients appear to survive significantly longer (p &lt, 0.001) if they receive individualized treatments after the exhaustion of standard of care treatments. Secondly, the tumor mutation landscape in Americans and Germans is congruent and thus warrants the transatlantic exchange of successful treatment protocols and the harmonization of guidelines.

Background: RNA-Seq from total RNA in FFPE tissue can be more challenging due to limited capture of partially degraded RNA. Exome-capture based RNA-Seq may circumvent such problems and allow reproducible complete molecular characterization of low-quality RNA from small clinical samples. Methods: HER2 negative patients within the GeparQuinto trial were treated with neoadjuvant anthracycline-taxane-based chemotherapy +/- bevacizumab. Patients with bevacizumab therapy had a significantly higher pCR rate, especially within the triple negative subgroup. We performed exome-capture RNA-Seq on 5µm FFPE sections from pre-therapeutic cores of 400 HER2 negative samples from this trial. In a prospectively planned, blinded study we correlated molecular subtypes and metagenes for proliferation, stroma, MHC2, and VEGFA with clinical and histopathological data. Molecular subtypes were defined using the AIMS methods. Metagenes were calculated as mean values corresponding to previously described gene clusters after platform transfer (Rody et al. 2011 PMID 21978456, Hu et al. 2009 PMID 19291283) and then z-transformed. Results: 296 samples with RNA-Seq data were classified as either of high (n=226) or of limited quality (n=70). For 22 samples RNA yield was insufficient and 82 did not pass initial QC. 121 (41%), 63 (21%), 34 (11.5%), 46 (15.5%), and 32 (11%) samples were defined as basal-like, HER2-enriched, luminal A, luminal B, and normal-like, respectively. Subtyping was robust with regard to gene filtering, normalization, and sample quality. ER and PR status from local IHC strongly correlated with gene expression (overall correctness 84% and 80% for ER, and 85% and 74% for PR, in samples with high and limited quality, respectively) and luminal subtypes (95% ER positive). Proliferation metagene correlated with histological grade (median -0.73, -0.39, and 0.53 in G1, G2, and G3, respectively; P Conclusions: Exome-capture RNA-Seq allows robust genomic characterization of clinical samples with limited FFPE material from core biopsies, and molecular subtypes and immune metagenes are predictive for pCR. The VEGFA metagene is a specific predictor for response to neoadjuvant bevacizumab treatment. Citation Format: Karn T, Meissner T, Weber K, Sinn B, Denkert C, Budczies J, Nekljudova V, Fasching PA, Holtrich U, Schem C, Solbach C, Hartmann A, Röcken C, Untch M, Young BM, Willis S, Leyland-Jones B, von Minckwitz G, Loibl S. Blinded molecular subtyping analysis from RNA-Seq of FFPE samples in the GeparQuinto trial reveals predictive value of VEGFA metagene for bevacizumab treatment [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-09-02.

Background Cancer is a genetic disease in which cells may accumulate somatic mutations. The measurement of the frequency these somatic mutations occur is referred to as tumor mutational burden or TMB. A minority of these mutations may give rise to neoantigens that could in turn be recognized and targeted by the immune system. The more somatic mutations a tumor develops, the more likely these neoantigens are to form. Recent studies, namely KEYNOTE-158, found high TMB status to be associated with improved outcomes in patients receiving pembrolizumab monotherapy. Materials and Methods This was a single-center retrospective study that analyzed 73 patients who received cancer treatment at Avera Cancer Institute between June 2016 and July 2019. Patients were included/excluded from the final analysis based on having received at least one dose of pembrolizumab, ipilimumab, and/or nivolumab. Information was collected on patient characteristics, treatment, and clinical outcomes. The primary outcomes were complete and partial response rates and were assessed objectively based on a composite of radiographic reports, physician documentation, and treatment course. Secondary outcomes were clinical outcomes in patients with PD-1 and/or PD-L1 expression, clinical outcomes in patients receiving mono-immunotherapy vs combination therapy, and clinical outcomes based on the line of treatment in which immunotherapy was utilized. Results A total of 34 patients met the inclusion criteria of having a high TMB value (10 mutations/megabase) and having received one dose of pembrolizumab, ipilimumab, or nivolumab. Primary Outcomes•16 patients experienced a partial response (AR = 0.47) and 6 of these patients experienced a complete response (AR = 0.17) Secondary Outcomes•Absolute risk with prior TMB information (PR 0.43, CR: 0.17)•Absolute risks for monotherapy (PR: 0.375, CR: 0.125) vs combination therapy (PR: 0.556, CR: 0.375)•Average line of treatment for TF, PR, and CR were 2.24, 2, and 1.83 respectively. Discussion Studies like KEYNOTE-158 found a significant correlation between high TMB and improved clinical outcomes in patients receiving checkpoint inhibitors. However, in this cohort of patients high TMB status was not a good predictor of therapeutic success compared to the data presented in KEYNOTE-001, CHECKMATE-057, OAK, or PACIFIC. While there are limitations associated with the sample size, the data support that checkpoint therapy benefits patients when it is used earlier in their treatment course and combined with other treatments. Further exploration is required to determine the most appropriate cut point for calling a patient TMB high as well as how TMB and PD-L1 predict response to specific therapies. Citation Format: Khai Nguyen, Bing Xu, Rachel Elsey, Tobias Meissner, Casey Williams. A single-center retrospective review assessing the use of tumor mutation burden as a predictor of therapeutic success in patients receiving immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 371.

Purpose Obesity has been shown to be strongly associated with the development of breast and gynecologic cancers. PIK3CA is one of the most commonly altered genes in solid tumors, and patients who have alterations are routinely initiated on therapies that target this pathway. However, response can vary greatly between patients, and the reason for differences in response has yet to be completely understood. Obesity has been shown to alter cancer metabolism and further dysregulate the PI3K/AKT/mTOR pathway which may affect response to target therapy. L. Cantley and colleagues suggest that the state of consistent hyperinsulinemia, which is common in obesity, can compromise the clinical benefit of PI3K-pathway targeted therapy, as insulin is a known potent stimulator of this pathway. Therefore, the purpose of this study was to compare clinical outcomes between obese and non-obese patients on pathway-directed therapy. Methods A retrospective analysis was performed on 140 patients with breast, ovarian, cervical, or endometrial malignancies at Avera Cancer Institute. Female patients with a documented oncogenic PIK3CA alteration were included in the study. Overall survival (OS) and progression free survival (PFS) were the primary endpoints. BMI groups were determined by using existing clinical categories of obesity as defined by the CDC. Patients were stratified into 2 BMI groups: Group A 25 kg/m2 and Group B 25 kg/m2. The Kaplan-Meier method was used to derive time-to-event estimates and test for significance. A cox proportional-hazard model was utilized to determine the association between survival time for patients with BMI being the predictor variable. Results Of the 140 patients, 106 patients had breast cancer, 21 patients had endometrial cancer, 9 patients had ovarian cancer, 3 patients had cervical cancer, and 1 patient had vaginal melanoma. There were 52 patients in Group A and 88 patients in Group B. 25 of the 140 patients had documented PIK3CA alterations but were never initiated on targeted therapy. Group B was found to reduce the hazard by a factor of 0.63 (p=0.09). The subset of Group B with PI3K/mTOR targeted therapy was found to reduce the hazard by a factor of 0.43 (p=0.008). Among the 43 patients who were included in PFS analysis, it was found that there was no statistically significant (p=0.6) improved PFS between the two groups. Conclusion In this analysis, patients with a PIK3CA mutation and who had a higher BMI had an improved response rate from targeted therapy and overall survival than compared to patients with a lower BMI. Treatment lines prior to initiation of targeted therapy varied greatly, and differences in the multiple types of malignancies studied in this analysis was a limitation. More prospective studies that are statistically powered are warranted in order to assess the true impact of obesity on improved OS compared to patients with a BMI 25. Citation Format: Diana Kim, Bing Xu, Rachel Elsey, Tobias Meissner, Casey Williams. Clinical outcomes between obese and non-obese breast and gynecological cancer patients with alterations in the PI3K/AKT/mTOR pathway [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1385.

Summary Multiple myeloma (MM) is a malignancy of plasma cells. Genome-wide association studies have shown that variation at 5q15 influences MM risk. Here, we have sought to decipher the causal variant at 5q15 and the mechanism by which it influences tumorigenesis. We show that rs6877329 G > C resides in a predicted enhancer element that physically interacts with the transcription start site of ELL2. The rs6877329-C risk allele is associated with reduced enhancer activity and lowered ELL2 expression. Since ELL2 is critical to the B cell differentiation process, reduced ELL2 expression is consistent with inherited genetic variation contributing to arrest of plasma cell development, facilitating MM clonal expansion. These data provide evidence for a biological mechanism underlying a hereditary risk of MM at 5q15., Graphical Abstract, Highlights • SNP rs6877329 underlies the association between 5q15 and multiple myeloma (MM) • SNP rs6877329 forms a chromatin-looping interaction with the ELL2 promoter • rs6877329-C risk allele reduces enhancer activity in MM • rs6877329-C confers lower ELL2 expression in MM patients, Li et al. find that rs6877329 underlies the 5q15 MM risk locus. Functional data demonstrate that rs6877329 resides within an enhancer that physically interacts with the ELL2 promoter. The rs6877329-C risk allele reduces enhancer activity and is associated with reduced ELL2 expression in MM patients.

Introduction Next generation sequencing has facilitated the understanding of pathogenesis and molecular heterogeneity of breast cancer (BC) as well as accelerated the path towards precision medicine. DNA sequencing (DNA-Seq) based assays for the detection of mutations and alterations in solid and hematologic cancers are finding their way into clinical practice and are readily available as clinical products. RNA sequencing (RNA-Seq), so far being vastly applied in the research context, promises to expand the diagnostic, prognostic and therapeutic use of this technology in cancer. Beyond mutational status, RNA-Seq enables the detection of fusions, quantification of gene expression level, detection of differentially expressed genes, molecular based subtyping, and risk-stratification. In this study we analyzed RNA-Seq and copy number data from BC patients that had undergone DNA-Seq based diagnostics through commercial providers with the goal to detect additional actionable targets. Materials and Methods We included 18 BC patients (5/18 triple negative) that had previously undergone DNA-based targeted (321 genes) sequencing. RNA-Seq to a minimum of 75M reads (75pb) was performed using 100 ng of total RNA on the Illumina NextSeq 500 platform. STAR was used for alignment (hg19) and gene expression quantification (RefSeq). Fusions were detected using STAR-Fusion. DESeq2 was utilized to identify patient specific differentially expressed genes by analyzing samples individually against a set of 13 controls from healthy breast tissue generated in-house. Copy number variations (CNVs) were detected using the Nanostring CNV Cancer panel (89 genes) on the Nanostring nCounter platform. Differentially upregulated or amplified genes were queried against DGIdb and Gene Drug Knowledge database for suitable drug matches, limiting the queries to clinically actionable antineoplastic drugs. Results Analyzing the cohort of 18 BC patients, we detected on average 26 BC relevant genes (526 total, log2 FC > 2) to be upregulated per patient. Querying the upregulated genes against DGIdb, we found a total of 18 genes that had drug matches and fulfilled the criteria of being actionable antineoplastic drugs, with 17/18 samples having a minimum of two gene targets (avg: 4). Most frequent upregulated genes were TOP2A (83%), AURKA (61%), AURKB (56%), RET (39%)and FGFR3 (28%). In the case of CNVs, 12/18 patients showed at least one gene target with clinically actionable drugs associated. This was observed across 12 gene targets that were amplified (avg: 3) and 4 gene targets that underwent deletions (avg: 1). Most frequent CNVs included MYC (14%) and CCND1 (12%). 4/7 patients having an AURKA overexpression also showed an AURKA amplification on the CNV assay. 10/18 patients had fusions events, with an average of three fusions per patient, including GAB2-WNT11, PAK1-TENM4 and FGFR2-CEP55 fusions. Conclusions We show that RNA-Seq and copy number assays provide additional clinical value by detecting suitable drug targets beyond traditional DNA-based approaches. We are conducting further analysis on how these additionally derived drug targets could improve the current treatment schedule of those patients. Citation Format: Meissner T, Amallraja A, Mark A, Andrews A, Connolly C, Young B, De P, Williams C, Leyland-Jones B. The value of RNA-Seq for the detection of clinically actionable targets in breast cancer - A small cohort analysis [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-05-22.

Introduction Variant calling based on DNA samples has been the gold standard of clinical testing since the advent of Sanger sequencing. The use of DNA variants has proved a great value to guide treatment in cancer patients. However, DNA based analysis will not inform about expression status of the gene harboring a particular variant. RNA has long been used to monitor expression. To this point RNA assays and analysis are confined to the research laboratory and rarely used clinically except in specifically defined gene signatures such as PAM50 and OncoType Dx. Beyond expression, RNA has the ability to confirm expression of DNA variants and identify fusion events. We hypothesize that the combination of DNA and RNA based data will allow the determination of variant specific expression status and improve clinical diagnostics. It has been previously shown that RNA sequencing (RNA-Seq) based variant calls are highly accurate and confirm DNA based variant calls. In this study we investigated the utility of RNA-Seq as a diagnostic assay integrated with DNA based sequencing data. Materials and Methods Targeted DNA sequencing of 321 genes was performed on 37 patient samples (FFPE), including 22 breast cancer samples by a commercial vendor. RNA-Seq on the same patient samples was performed using 100ng of total RNA. Libraries were run on the Illumina NextSeq 500 with a minimum of 75M paired 75bp reads. To evaluate RNA-seq expression reproducibility, replicates of 6 normal ovarian tissue samples (min. 50M reads) were run in sets of triplicates. STAR was used for alignment (hg19) and gene expression quantification (RefSeq). RNA-Seq based variant calling was performed using the SNPiR pipeline. Based on the results of the commercial assay, DNA based variants were examined for expression of the corresponding genes and ability to confirm variants in the RNA-Seq data. Results RNA expression data showed no corresponding gene expression for at least one single nucleotide variant (SNV) in 9/37 patients analyzed (24.3%). In 18/37 patients (48.6%) SNV corresponding expression was in the lowest quartile of expression values. Variant calls could be confirmed by RNA-Seq for 95/455 SNVs, with adequate coverage in 263 of the remaining 360 variant locations (median coverage: 34). Of these, a homozygous reference call was made in 166/263 SNVs. Concordance for RNA-Seq gene level expression data between replicates was > 0.995. Conclusions These findings suggest that RNA-Seq based data can provide clinical value when using gene expression values in combination with DNA based variant calls. We found gene level expression to be highly reproducible and will further investigate the use of spike in controls to determine clinically usable expression ranges and lower limit of expression values. To our knowledge, it has not been shown that RNA-Seq based variant calls are reproducible which is the focus of our current research as this will be one requirement for usage in a regulated environment. While our use of RNA Seq is currently limited to gene expression level data, we have demonstrated a clinically relevant benefit to using RNA Seq data as an additive feature to the current standard of DNA variant calling. Citation Format: Young B, Mark A, Meissner T, Amallraja A, Andrews A, Connolly C, Williams C, Leyland-Jones B. Utilities and challenges of RNA-Seq based expression and variant calling in a clinical setting [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-05-23.

Introduction Metastasis is the primary cause of cancer-related death, with genomic evolution between primary and metastatic breast cancer only recently being studied in smaller numbers. We reconstructed the tumor evolution of a female patient originally diagnosed with localized ER+ HER2- disease to investigate the molecular changes that triggered relapse after long term aromatase inhibitor (AI) therapy and tracked them during subsequent treatments. The patient was diagnosed in 2002, relapsed in 2012 (regional lymph node), progressed in 2015 (liver metastasis), and passed away in 2018. We performed DNA- and RNA-sequencing on FFPE tumor samples and multiple liquid biopsies. The patient underwent eight lines of therapy including genome guided targeted therapy. Materials and Methods Whole exome and transcriptome sequencing was performed on the patient's primary tumor site at diagnosis (T1) and a metastatic lymph node (M1). Targeted DNA sequencing was performed on a metastatic liver sample (M2, 315 genes) and on liquid biopsies (n=11, 70 genes) every 2-3 months from 2015-2018. Results The mutational landscape between timepoints T1, M1 and M2 revealed low mutational burden at diagnosis which ultimately doubled at progression with active mutational processes (APOBEC, POLE) showing concordance between timepoints. DNA-based analysis revealed multiple mutations in the PI3K-Akt signaling pathway. Two mutations were shared between the samples: a clonal PIK3CA Q546R and a TP53 E180K mutation detected as clonal in M1/M2 and subclonal in T1. Individual samples carried private driver mutations, ranging from n=4 in T1, n=7 in M1 to n=16 in M2. While T1 harbored a single clonal driver mutation, with additional driver mutations being subclonal, M1 and M2 harbored multiple clonal driver mutations and a smaller percentage of subclonal driver mutations. A phylogenetic tree showed branching off between T1 and M1/M2. Liquid biopsies, while initially negative, started to detect mutations found among T1, M1 and M2, with increasing allele frequency (AF) throughout time. By the end of 2016, an ESR1 E380Q mutation not previously detected was found at a low AF that had rapidly increased by the end of 2017. RNA-Seq analysis revealed 908 (209 up, 699 down) genes to be differentially expressed between T1 and M1. Most significantly downregulated genes in M1 were TFF1 and PGR indicating loss-of-sensitivity / resistance to AI therapy. Most upregulated genes in M1 included PTHLH, S100P, and SOX2 which have been implicated in promoting tumor growth and metastasis. Pathway analysis between T1 and M1 showed enrichment for the hallmark gene sets ‘Estrogen Response early and late’ and ‘Epithelial mesenchymal transition’. Conclusions This phylogenetic reconstruction of the life history of a single patient's cancer revealed an increased mutational rate over time between the patient's primary tumor at diagnosis and samples taken at relapse; and a high level of heterogeneity with metastatic sites sharing only one driver mutation with the primary tumor at diagnosis and having acquired multiple de-novo driver mutations. Transcriptome profiling aided in uncovering the mechanisms that lead to the patient's initial relapse, indicating expression profile changes that promote tumor progression and metastasis as well as reduced sensitivity / resistance to the patients AI based therapy. Monitoring tumor progression through frequent liquid biopsies allowed for the detection of mutations from multiple metastatic sites, in addition to the detection and growth of an AI resistant clone harboring an ESR1 E380Q mutation that was not present in any of the solid tissue samples. Citation Format: Bing Xu, Anu Amallraja, Padmapriya Swaminathan, Rachel Elsey, Christel Davis, Stephanie Theel, Sarah Viet, Jason Petersen, Amy Krie, Gareth E Davies, Casey B Williams, Erik Ehli, Tobias Meissner. Case report - 16 year life history and genomic evolution of an ER+ HER2- breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-05-20.

Purpose: In breast cancer, bevacizumab increased pCR rate but not long-term survival and no predictive markers are available to identify patients with long-term benefit from the drug. Experimental Design: We profiled 289 pretherapeutic formalin-fixed, paraffin-embedded (FFPE) biopsies of HER2-negative patients from the GeparQuinto trial of neoadjuvant chemotherapy ± bevacizumab by exome-capture RNA-sequencing (RNA-seq). In a prospectively planned study, we tested molecular signatures for response prediction. IHC validation was performed using tissue microarrays. Results: We found strong agreement of molecular and pathologic parameters as hormone receptors, grading, and lymphocyte infiltration in 221 high-quality samples. Response rates (49.3% pCR overall) were higher in basal-like (68.9%) and HER2-enriched (45.5%) than in luminal B (35.7%), luminal A (17.9%), and normal-like (20.0%) subtypes. T-cell (OR = 1.60; 95% confidence interval, 1.21–2.12; P = 0.001), proliferation (OR = 2.88; 95% CI, 2.00–4.15; P < 0.001), and hypoxia signatures (OR = 1.92; 95% CI, 1.41–2.60; P < 0.001) significantly predicted pCR in univariate analysis. In a prespecified multivariate logistic regression, a small hypoxia signature predicted pCR (OR = 2.40; 95% CI, 1.28–4.51; P = 0.006) with a significant interaction with bevacizumab treatment (P = 0.020). IHC validation using NDRG1 as marker revealed highly heterogenous expression within tissue leading to profound loss of sensitivity in TMA analysis, still a significant predictive value for pCR was detected (P = 0.025). Conclusions: Exome-capture RNA-seq characterizes small FFPE core biopsies by reliably detecting factors as for example ER status, grade, and tumor-infiltrating lymphocytes levels. Beside molecular subtypes and immune signatures, a small hypoxia signature predicted pCR to bevacizumab, which could be validated by IHC. The signature can have important applications for bevacizumab treatment in different cancer types and might also have a role for novel combination therapies of bevacizumab with immune checkpoint inhibition.

Metastatic breast cancer is one of the leading causes of cancer-related death in women. Limited studies have been done on the genomic evolution between primary and metastatic breast cancer. We reconstructed the genomic evolution through the 16-yr history of an ER+ HER2− breast cancer patient to investigate molecular mechanisms of disease relapse and treatment resistance after long-term exposure to hormonal therapy. Genomic and transcriptome profiling was performed on primary breast tumor (2002), initial recurrence (2012), and liver metastasis (2015) samples. Cell-free DNA analysis was performed at 11 time points (2015–2017). Mutational analysis revealed a low mutational burden in the primary tumor that doubled at the time of progression, with driver mutations in PI3K–Akt and RAS–RAF signaling pathways. Phylogenetic analysis showed an early branching off between primary tumor and metastasis. Liquid biopsies, although initially negative, started to detect an ESR1 E380Q mutation in 2016 with increasing allele frequency until the end of 2017. Transcriptome analysis revealed 721 (193 up, 528 down) genes to be differentially expressed between primary tumor and first relapse. The most significantly down-regulated genes were TFF1 and PGR, indicating resistance to aromatase inhibitor (AI) therapy. The most up-regulated genes included PTHLH, S100P, and SOX2, promoting tumor growth and metastasis. This phylogenetic reconstruction of the life history of a single patient's cancer as well as monitoring tumor progression through liquid biopsies allowed for uncovering the molecular mechanisms leading to initial relapse, metastatic spread, and treatment resistance.

Introduction Despite many advances, the treatment for most patients with advanced solid tumors in the abdomen continues to be a clinical challenge. Copy number variants (CNVs) are an important part of the genomic landscape of these patients. Next-generation sequencing (NGS) tests used in the clinical setting are predominantly multi-gene targeted panels (FM, Guardant, NeoGenomics). Although this is the current standard, there is still doubt about the reliability and accuracy of such panels (PMID: 28472276), and panel based CNV-calling in clinical labs use stringent thresholds that may filter out many CNVs. The purpose of our study was to evaluate the clinical utility of a 125-gene targeted sequencing panel in detecting actionable CNVs in a cohort of colorectal cancer patients, with the eventual goal of identifying appropriate genome-guided therapy options. Materials and Methods We performed (i) targeted (350X) NGS based on the IDT xGen 125-gene Pan-Cancer panel on tumor and matched normal blood samples of 54 patients as well as (ii) very low-coverage (LC-WGS, 0.1X) whole genome sequencing on tumor and matched blood samples for the same set of patients. Bioinformatics methods included adapter trimming with bbduk, alignment with bwa-mem, quality control with FASTQC and Qualimap. CNVkit, ichorNCA, and Control-FREEC were used to call CNVs. For CNVkit and ichorNCA, the normal samples were combined to create a ‘reference normal'. Multiple window sizes were utilized to call CNVs at different scales. Results We compared the results of CNV calls from CNVkit, ichorCNA and Control-FREEC from targeted and LC-WGS data. This analysis indicates that targeted panels are at least currently not capable of reliably identifying the copy number landscape of clinical colorectal samples when matched normal samples are utilized. While within the targeted region we identified a median of five clinically actionable CNVs per sample for both targeted and whole genome sequencing (CNV amplification threshold of 5 copies), discordance of called CNVs on average was 56 percent. Numerical copy number calls tend to be higher for the targeted panel (mean: 10) vs LC-WGS (mean: 6) Beyond the targeted regions, LC-WGS detected a median of one additional clinical actionable CNV in genes such as PD-L1, JAK2, LRP1B, VEGFA, BCL2, and CCND3 which are not part of the targeted gene panel. Conclusions Our analysis suggests that CNV calling from targeted panels can be improved upon by incorporating very low-coverage whole genome sequencing. Citation Format: Anu Amallraja, Janina Fuß, Casey B. Williams, Michael Forster, Sebastian Hinz, Tobias Meißner, Alexander Hendricks. Very low coverage whole genome sequencing improves clinically relevant copy number variation calling compared to targeted sequencing [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 3617.

International audience; An amendment to this paper has been published and can be accessed via a link at the top of the paper.

// Michael Forster 1 , Adam Mark 1, 2 , Friederike Egberts 3 , Elisa Rosati 1 , Elke Rodriguez 3 , Martin Stanulla 4 , Dirk Bauerschlag 5 , Christian Schem 6 , Nicolai Maass 5 , Anu Amallraja 7 , Karla K. Murphy 8 , Bruce R. Prouse 8 , Raed A. Sulaiman 8 , Brandon M. Young 7 , Micaela Mathiak 9 , Georg Hemmrich-Stanisak 1 , David Ellinghaus 1 , Stephan Weidinger 3 , Philip Rosenstiel 1 , Norbert Arnold 1, 5 , Brian Leyland-Jones 7 , Casey B. Williams 7 , Andre Franke 1, * and Tobias Meisner 7, * 1 Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany 2 Current address: Center for Computational Biology and Bioinformatics, Department of Medicine, University of California, San Diego, CA, USA 3 Department of Dermatology, Schleswig-Holstein University Hospital, Kiel, Germany 4 Department of Pediatric Haematology and Oncology, Hannover Medical School, Hannover, Germany 5 Department of Gynaecology and Obstetrics, Schleswig-Holstein University Hospital, Kiel, Germany 6 Mammazentrum Hamburg, Hamburg, Germany 7 Department of Molecular and Experimental Medicine, Avera Cancer Institute, Sioux Falls, SD, USA 8 Pathology, Avera McKennan, Sioux Falls, SD, USA 9 Department of Pathology, Schleswig-Holstein University Hospital, Kiel, Germany * These authors share last authorship Correspondence to: Michael Forster, email: m.forster@ikmb.uni-kiel.de Keywords: breast cancer sequencing; normal RNA expression; drug selection; formalin fixed paraffin embedded; fresh frozen Received: March 07, 2018 Accepted: August 04, 2018 Published: August 17, 2018 ABSTRACT Background: While standard RNA expression tests stratify patients into risk groups, RNA-Seq can guide personalized drug selection based on expressed mutations, fusion genes, and differential expression (DE) between tumor and normal tissue. However, patient-matched normal tissue may be unavailable. Additionally, biological variability in normal tissue and technological biases may confound results. Therefore, we present normal expression reference data for two sequencing methods that are suitable for breast biopsies. Results: We identified breast cancer related and drug related genes that are expressed uniformly across our normal samples. Large subsets of these genes are identical for formalin fixed paraffin embedded samples and fresh frozen samples. Adipocyte signatures were detected in frozen compared to formalin samples, prepared by surgeons and pathologists, respectively. Gene expression confounded by adipocytes was identified using fat tissue samples. Finally, immune repertoire statistics were obtained for healthy breast, tumor and fat tissues. Conclusions: Our reference data can be used with patient tumor samples that are asservated and sequenced with a matching aforementioned method. Coefficients of variation are given for normal gene expression. Thus, potential drug selection can be based on confidently overexpressed genes and immune repertoire statistics. Materials and Methods: Normal expression from formalin and frozen healthy breast tissue samples using Roche Kapa RiboErase (total RNA) (19 formalin, 9 frozen) and Illumina TruSeq RNA Access (targeted RNA-Seq, aka TruSeq RNA Exome) (11 formalin, 1 frozen), and fat tissue (6 frozen Access). Tumor DE using 10 formalin total RNA tumor samples and 1 frozen targeted RNA tumor sample.

The increasing applicability and sensitivity of next generation sequencing methods exacerbate one of the main issues in the molecular biology laboratory, namely cross-sample contamination. This type of contamination, which could massively increase the rate of false-positive calls in sequencing experiments, can originate at each step during the processing of multiple myeloma samples, such as CD138-selection of tumor cells, RNA and DNA isolation or the processing of sequencing libraries. Here we describe a Droplet Digital PCR (ddPCR) method and a simple bioinformatic solution for the detection of contamination in patient's samples and derived sequencing data, which are based on the same principle: detection of alternative alleles for single-nucleotide polymorphisms (SNPs) that are homozygous according to the control (germ line) sample.

Recently, we identified ELL2 as a susceptibility gene for multiple myeloma (MM). To understand its mechanism of action, we performed expression quantitative trait locus analysis in CD138+ plasma cells from 1630 MM patients from four populations. We show that the MM risk allele lowers ELL2 expression in these cells (Pcombined = 2.5 × 10−27; βcombined = −0.24 SD), but not in peripheral blood or other tissues. Consistent with this, several variants representing the MM risk allele map to regulatory genomic regions, and three yield reduced transcriptional activity in plasmocytoma cell lines. One of these (rs3777189-C) co-locates with the best-supported lead variants for ELL2 expression and MM risk, and reduces binding of MAFF/G/K family transcription factors. Moreover, further analysis reveals that the MM risk allele associates with upregulation of gene sets related to ribosome biogenesis, and knockout/knockdown and rescue experiments in plasmocytoma cell lines support a cause–effect relationship. Our results provide mechanistic insight into MM predisposition., ELL2 was recently discovered as a susceptibility gene for multiple myeloma (MM). Here, they show that the MM risk allele lowers ELL2 expression in plasma cells, that it also upregulates gene sets related to ribosome biogenesis, and that one of the linked variants reduces binding of MAFF/G/K family transcription factors.

Background. Angiogenesis is a fundamental process underlying cancer progression and autoimmune disease. Lewis Y is known as a regulated glycan-structure supporting human endothelial function and angiogenesis. Objectives. We hypothesize that Lewis Y based analogues interfere with Lewis Y mediated endothelial functions and angiogenesis. We therefore evaluated the ability of 3, 4-bis [(b-D-galactopyranosyl)osy]-methyl-furan (BGF) a furan-based Lewis-Y saccharide mimetic to inhibit human endothelial adhesion, migration and in vitro angiogenesis. Material and Methods. The ability of BGF and additional furan-based saccharide-mimetics was investigated to inhibit adhesion and migration of human bone marrow endothelial cells (HBMEC). Influence of BGF was tested on a multicelluar in vitro – angiogenesis assay in the presence of VEGF. Results. BGF significantly inhibited HBMEC adhesion and migration stimulated by TNF-alpha by up to 70%. The anti-adhesive effect of BGF was particularly evident when HBMEC adhesion and migration was tested on collagen as extracellular matrix with weaker effect when laminin and fibronectin were used as an extracellular matrix. BGF was ineffective when HBMEC were stimulated with VEGF. The inhibition of endothelial function translated into a significant inhibitory effect of BGF in the multicellular in vitro angiogenesis-assay. BGF reduced the angiogenesis index compared to the positive controls by 32%. Conclusions. We identified the ability of the furan-based Lewis Y saccharide mimetic BGF as a specific modulator of TNF-alpha activated endothelial function and in vitro angiogenesis. BGF and other related glycan analogues should further be explored for their ability to down modulate endothelial activation in TNF-alpha driven pathophysiologic conditions in autoimmune disease and cancer indications (Adv Clin Exp Med 2015, 24, 5, 759–768).

In multiple myeloma malignant plasma cells expand within the bone marrow. Since this site is well-perfused, a rapid dissemination of “fitter” clones may be anticipated. However, an imbalanced distribution of multiple myeloma is frequently observed in medical imaging. Here, we perform multi-region sequencing, including iliac crest and radiology-guided focal lesion specimens from 51 patients to gain insight into the spatial clonal architecture. We demonstrate spatial genomic heterogeneity in more than 75% of patients, including inactivation of CDKN2C and TP53, and mutations affecting mitogen-activated protein kinase genes. We show that the extent of spatial heterogeneity is positively associated with the size of biopsied focal lesions consistent with regional outgrowth of advanced clones. The results support a model for multiple myeloma progression with clonal sweeps in the early phase and regional evolution in advanced disease. We suggest that multi-region investigations are critical to understanding intra-patient heterogeneity and the evolutionary processes in multiple myeloma., In multiple myeloma, malignant cells expand within bone marrow. Here, the authors use multi-region sequencing in patient samples to analyse spatial clonal architecture and heterogeneity, providing novel insight into multiple myeloma progression and evolution.