EXTH-96. BIOPROCESSING OF SURGICAL PEDIATRIC BRAIN TUMOR SPECIMENS FOR GENOME-GUIDED PERSONALIZED DRUG TESTING

Novel treatment approaches for pediatric central nervous system (CNS) tumors are urgently needed. A lack of patient-derived tumor cells impedes progress towards developing such new therapies. We intended to overcome this challenge by establishing methods to create a biorepository of viable single cell suspensions of pediatric brain tumor surgical specimens. Quantitative and qualitative comparisons of tissue processing strategies were performed to preserve viability of heterogeneous tumor and immune cells. Novel drug targets were identified by analyzing pathways affected by RNA transcripts that are highly expressed (outliers) in a patients’ tumor; outliers for each patient were determined by RNA-seq data from individual patients’ tumors compared with a compendium of 12,747 pediatric and adult samples harmonized by the Treehouse Childhood Cancer Initiative at the UCSC Genomics Institute. The predicted anti-tumor efficacy of small molecule inhibitors of the outlier pathways was tested in cell viability assays against short-term cultured cells from matched patients. Successful tissue collection required obtaining informed consent, standard operating procedures, and sample recording using a Laboratory Inventory Management Software (LIMS). Since 2020 we have banked 67 pediatric CNS brain tumor specimens at Stanford. Amongst those, 51 cases yielded sufficient tissue for RNA-seq and cryoprotection. The most common tumor histology was low-grade glioma (LGG, 26 of 67), the majority of which were pilocytic astrocytoma (18 of 26). The second and third most common tumor types are embryonal tumors (6 medulloblastoma, 3 AT/RT) and ependymoma (4), respectively. We identified significant differences in cell viability with different preservation media. An outlier pathway previously not implicated in LGG was identified and sensitivity to a small molecule inhibitor of this outlier pathway was demonstrated. Taken together, we established feasibility for validating therapeutic vulnerabilities identified by a genome-guided approach in short-term cultures from surgical specimens. This works facilitates the rapid development of personalized CNS tumor treatment.

Abstract 2287: Detection of RNA-Seq library preparation type via random forest

Library preparation protocols for RNA sequencing (RNA-Seq) libraries vary and affect the downstream analysis of RNA-Seq data. Available RNA sequencing datasets often lack library construction protocol information in the metadata. This information is necessary to be able to compare RNA sequencing datasets appropriately. For example, non-polyadenylated transcripts are measured when a ribosomal RNA-depletion method is used (riboD library preparation), but not when transcripts are selected by poly(A) tails (polyA library preparation). Without knowing the library preparation method, it would appear that the non-polyadenylated transcripts were expressed at much higher levels in the samples prepared via riboD. In order to tackle this issue, we have developed a Random Forest classifier that can delineate between riboD and polyA RNA-Seq datasets. A grid search method was applied to the number of trees, maximum depth, and the minimum datasets included in each leaf node, which determined the best parameters to be 100, 8, and 1 respectively. However, applying the model on the Pediatric Brain Tumor Atlas (PBTA) showed strong overfitting for polyA samples. We examined the performance of the model investigating each maximum depth increment from 1 to 8, and we determined that the best performance on our validation set was achieved with maximum depth of 1. We subsequently proceeded to train our classifier on our own curated compendiums of pediatric cancer polyA and riboD samples including 188 and 264 samples respectively, after balancing the two datasets in terms of disease prevalence, and selecting the top 5,000 most variable genes as the default input dimensionality of the model. For samples whose genes do not exactly match the predetermined 5,000 genes, we substitute the expression of the missing gene with the mean expression of the gene observed in the training data. We show that it achieves 100% classification accuracy of samples to their respective library preparation protocols in GTEX (all polyA), CCLE (all polyA), and for 7 of 9 SRA projects. Five of the SRA projects contained only riboD datasets, 3 contained only all polyA datasets, and one was 50% riboD, and 50% polyA. Notably the SRA datasets are not all cancer related datasets, showing the power of our model to distinguish between library preparation protocols in vastly different settings. The model will become available on Docker so that it is readily, and easily accessible for application on new samples. Our model serves as an important step towards robust library preparation identification. Including samples in the training procedure from diverse contexts would make our model more widely applicable. Need summary statement here and/or future work statement

Citation Format: Ioannis Anastopoulos, Holly Beale, Geoff Lyle, Allison Cheney, Olena M. Vaske, Joshua M. Stuart. Detection of RNA-Seq library preparation type via random forest [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 2287.

EPCO-23. COMPARATIVE TRANSCRIPTOMICS TO IDENTIFY TARGETED THERAPY CANDIDATES IN HIGH GRADE GLIOMA

Genomic characterization is often used for the identification of therapeutic targets in tumors. Recently, comparative transcriptomics has begun to be utilized for this purpose. In this pilot, we compare the transcriptome of a patient with recurrent high grade glioma (HGG) to our cohort to identify potential therapies. We reviewed transcriptomic profiles from patients who had resection of HGG at our institution over the past year as well as the UCSC cancer compendium. Briefly, tumor RNA was extracted from embedded tumor tissue sections with tumor cellularity higher than 20%. RNA libraries were sequenced to obtain approximately 65 million reads on an Illumina HiSeq 4000 System utilizing patterned flow cell technology. The RNA profile of a 24 male with Li-Fraumeni syndrome and recurrent HGG with leptomeningeal spread underwent comparative transcriptomics to identify targets. A Bayesian statistical framework for gene expression outlier detection was used. These comparisons allowed for the identification of genes and pathways that are significantly overexpressed. Our internal HGG cohort consisted of 44 adult patients and was evenly distributed among the 4 HGG Verhaak subtypes. Our patient of interest had druggable outlier expression in HDAC1, STAT1 and STAT2 in comparison to our internal cohort indicating vorinostat and ruxolitinib as potential therapies, respectively. We then compared our patient of interest to 12,747 patients in the cancer compendium and STAT2 expression was high but not an outlier. In comparison to 738 glioma samples, STAT1 and STAT2 were outliers but not HDAC1 again indicating ruxolitinib as a potential targeted therapy. The patient did not have outlier expression in notch transcriptional targets or immune checkpoint biomarkers when compared to all cohorts. In conclusion, comparative Transcriptomics can identify therapeutic targets in a patient with recurrent HGG even in small cohorts. In our pilot, we identified ruxolitinib as a potential candidate to treat leptomeningeal recurrence.

Abstract 5464: Determining accuracy of RNA sequencing data for gene expression profiling of single samples

Gene expression analysis of single samples shows increasing promise for clinical applications. However, obtaining high quality RNA from a human tumor sample can be challenging because medical, surgical, and pathological requirements often lead to sparse or degraded RNA. The variability in RNA quality presents challenges for defining input sample requirements, which are required to calculate sensitivity, specificity and reference ranges as required for a Clinical Laboratory Improvement Amendments (CLIA)-approved test.

Clinical analysis of a single RNA-Seq dataset for the purpose of gene expression profiling involves not only the patient's sample, but a comparison cohort. We use 12,236 total tumor samples and require at least 20 samples for within-disease comparisons. Many of these samples do not have associated metadata about the quality of the sample, and so we have prioritized quality measures that can be derived from the sequence data alone.

In order to characterize variability present in RNA-Seq datasets, we analyzed paired-end Illumina RNA sequencing (RNA-Seq) data from 1088 tumor samples from 29 data providers. We categorized reads based on where and how well they map to the genome, as well as by their PCR duplicate status. We defined reference ranges for five types of reads found in sequencing data: unmapped (0-13%); multi-mapped (2-15%); mapped duplicate (2-66%); mapped non exonic (0-26%) and mapped, exonic, non-duplicate (MEND, 27-76%). Only 64% of the 1088 tumor samples had read type fractions within the reference ranges. Of the remainder, most exceeded the reference ranges of more than one type of read.

We then measured the relationship of sensitivity and specificity to input MEND read depth. We subsampled 5 deeply sequenced samples. With each subsample, we identified exceptionally highly expressed genes and samples with similar gene expression profiles. With subsampling to 20 million MEND reads, we detected over-expressed genes (“up-outlier” genes) with a median sensitivity of 96.1% and specificity of 99.8%; sample similarity had 96.6% sensitivity and 100.0% specificity. We estimate that a sample sequenced to a depth of 70 million total reads will typically have sufficient data for the up-outlier and sample-similarity gene expression analysis assays described here.

With this analysis, we have identified a conservative approach to measuring the quality of RNA-Seq read data, which can then be used to define the sensitivity and specificity of single-sample assays to support their ultimate clinical adoption.

Citation Format: Holly C. Beale, Jacquelyn M. Roger, Matthew A. Cattle, Liam T. McKay, Katrina Learned, Geoff Lyle, Ellen T. Kephart, Rob Currie, Du Linh Lam, Lauren Sanders, Jacob Pfeil, John Vivian, Isabel Bjork, Sofie R. Salama, David Haussler, Olena M. Vaske. Determining accuracy of RNA sequencing data for gene expression profiling of single samples [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 5464.

Identifying the spatial and temporal variability of economic opportunity costs to promote the adoption of alternative land uses in grain growing agricultural areas: an Australian example

Grain growers face many future challenges requiring them to adapt their land uses to changing economic, social and environmental conditions. To understand where to make on ground changes without significant negative financial repercussions, high resolution information on income generation over time is required. We propose a methodology which utilises high resolution yield data collected with precision agriculture (PA) technology, gross margin financial analysis and a temporal standardisation technique to highlight the spatial and temporal consistency of farm income. On three neighbouring farms in Western Australia, we found non-linear relationships between income and area. Spatio-temporal analysis on one farm over varying seasons found that between 37 and 49% (1082-1433ha) of cropping area consistently produced above the selected income thresholds and 43-32% (936-1257ha) regularly produced below selected thresholds. Around 20% of area showed inconsistent temporal variation in income generation. Income estimated from these areas represents the income forgone if a land use change is undertaken (the economic opportunity cost) and the average costs varied spatially from $190±114/ha to $560±108/ha depending on what scenario was chosen. The interaction over space and time showed the clustering of areas with similar values at a resolution where growers make input decisions. This new evidence suggests that farm area could be managed with two strategies: (a) one that maximises grain output using PA management in temporally stable areas which generate moderate to high income returns and (b) one that proposes land use change in low and inconsistent income returning areas where the financial returns from an alternative land use may be comparable. The adoption of these strategies can help growers meet the demand for agricultural output and offer income diversity and adaptive capacity to deal with the future challenges to agricultural production.

Mycobacterium avium infection in a patient with the acquired immunodeficiency syndrome and silicone breast implants

We describe a case of Mycobacterium avium-intracellulare infection in a silicone augmented breast of a female patient with the acquired immunodeficiency syndrome and disseminated Mycobacterium avium complex. Explantation and treatment with appropriate antibiotics led to resolution of the breast infection.

Immediate breast reconstruction in breast cancer: morbidity and outcome

Breast reconstruction is frequently performed for and requested by women with breast cancer. There are continued concerns about the safety of this procedure. We reviewed the Medical College of Georgia experience with immediate breast reconstruction to determine overall morbidity and whether premorbid risk factors could predict complications. Patients were reviewed with attention to epidemiologic characteristics, comorbid medical conditions, and risk factors; hospital and operative course; immediate wound complications; and patient survival. t test and chi-square analysis were performed to determine risk factors for developing wound complications. Between October 1990 and December 1996, 55 patients underwent 62 mastectomies and immediate reconstruction for breast cancer or contralateral prophylaxis. There were 13 stage 0, 23 stage I, 16 stage II, 4 stage III, and 1 stage IV tumors. There were 19 prosthetic and 43 autologous tissue reconstructions. Eighteen patients had 24 wound complications. Major complications occurred in eight patients and required reoperation for implant removal (two bilateral), ventral herniorrhaphy, and split thickness skin grafting for tissue loss. Patients who were obese were statistically more likely to develop surgical wound complications. Tobacco use, age, comorbid medical illness, operative blood loss, length of operation, and length of hospital stay did not predict for the development of wound complications. Patients who underwent prosthetic reconstruction had a significantly higher rate of major wound complications when compared with those who had autologous reconstruction. There was a single case of delay of chemotherapy secondary to surgical wound complication. There were no cases of autologous flap loss or local recurrence. Median survival is 23 months (1-72 months). At last follow-up, 53 patients are alive and without evidence of local recurrence. Breast reconstruction may be performed safely for most breast cancer patients. Autologous tissue reconstruction is preferred and carries significantly less major morbidity. Reconstruction should not delay adjuvant chemotherapy.

Aspergillus: a rare primary organism in soft-tissue infections

Nonclostridial necrotizing soft-tissue infections are usually polymicrobial, with greater than 90 per cent involving beta-hemolytic streptococci or coagulase-positive staphylococci. The remaining 10 per cent are usually due to Gram-negative enteric pathogens. We describe the case of a 46-year-old woman with bilateral lower extremity fungal soft tissue infections. She underwent multiple surgical debridements of extensive gangrenous necrosis of the skin and subcutaneous fat associated with severe acute arteritis. Histopathological examination revealed Aspergillus niger as the sole initial pathogen. Despite aggressive surgical debridement, allografts, and intravenous amphotericin B, her condition clinically deteriorated and she ultimately died of overwhelming infection. Treatment for soft-tissue infections include surgical debridement and intravenous antibiotics. More specifically, Aspergillus can be treated with intravenous amphotericin B, 5-fluorocytosine, and rifampin. Despite these treatment modalities, necrotizing fascitis is associated with a 60 per cent mortality rate. Primary fungal pathogens should be included in the differential diagnosis of soft-tissue infections.