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Performance of Single Cell RNA sequencing (scRNA-seq) experiments depends on multiple factors including the number of cells loaded, cell recovery rates, and sequencing coverage. We optimized such factors in 10x Genomics gene expression profiling and compared targeted panels to whole transcriptome sequencing using Human donor PBMC samples. We expect that our findings will apply to scRNA-seq studies of other sample types as well. Optimizing cell recovery in scRNA-seq experiments is essential in order to ensure that changes in low-abundance cell populations (e.g. Treg) can be accurately quantified. Statistical analysis indicated that at least 100 cells are needed to assess cell-type specific state changes. We expect to recover 40-65% of cells loaded into the 10x chip. We found that loading 20k-30k cells/lane combined with cell hashtags for improved doublet detection improved cell recovery over the recommended loading of 10K-16K cells. We also surveyed internal single cell experiments and observed variable cell recovery across different sample types. The amount of ambient RNA in the library was correlated with lower performance in single cell studies, suggesting that cell death or damage was causing lower recovery. We found that using PBS for cell resuspension (which is less likely to cause cell rupture) performs as well as the nuclease-free water suggested by 10x. We also assessed the impact of sequencing depth and protocol on scRNA-seq data quality. Sequencing depth impacts both experiment cost and data quality due to dropouts. We performed computational simulations of lower depth sequencing by subsampling various number of reads from PBMC experiments to obtain coverages of 10K, 20K, 40K and 80K reads per cell. We observed that 40K reads per cell, yielding about 70% sequencing saturation, provided a good balance between cost and sequencing depth. Finally, we compared dropout rates and cell type annotation for two targeted panels, the Human Gene Signature and Human Immunology panels to whole transcriptome scRNAseq. On average we detected only 200 genes out of over 1000 represented in each panel. Dropout rates for targeted panels were only reduced at low coverage; at read depths higher than 40K reads per cell the whole transcriptome and targeted panels had similar dropout rates. Both methods detected major immune cell types, but targeted sequencing could not accurately identify some subtypes due to low number of detected genes. We conclude that for immune cell profiling whole-transcriptome analysis at coverage of 40K reads per cell or higher with inputs of 20k-30k cells and use of sample hashtag antibodies provides the best balance of experiment cost, cell recovery and transcriptome coverage. Although these guidelines were established for Human PBMCs we expect similar outcomes with other complex cell mixtures such as dissociated tissues. Citation Format: Amir Bayegan, Julien Tessier, Emma Wang, Adalis Maisonet, Shu Yan, Shannon McGrath, Donald G. Jackson, Jack Pollard. Practical guidelines for the design of single cell sequencing studies [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 762.

Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related deaths worldwide, accounting for more than 700,000 deaths per year. Although transforming growth factor beta (TGF-β) can function as a tumor-suppressor in premalignant cells, patient data suggest that it plays a role in the progression of HCC. Recently, immunotherapy targeting PD-1 has been approved for HCC, however, success is limited to only ~17% objective response rate in the treated HCC patients. Analysis of genomic data from two HCC patient cohorts revealed that TGF-β pathway is highly activated in patients with advanced HCC. Collectively, these findings indicate that targeting TGF-β in combination with PD-1 blockade may be a compelling therapeutic strategy. Therefore, using two syngeneic mouse models of HCC (Hepa1-6 and H22), we examined the effect of a pan-TGF-β neutralizing antibody (1D11) in combination with PD-1 blockade. Results from our study show that co-inhibiting TGF-β with PD-1 resulted in significant tumor growth inhibition in both HCC models. Flow cytometry analysis of the tumor revealed the combination treatment group exhibited a reduction in potentially suppressive tumor-associated-macrophages relative to either single agent groups. In addition, we show that combination therapy was associated with increase in the intra-tumoral levels of proinflammatory cytokines - IFNγ, IL-1β, TNF-α as well as enhanced expression of anti-tumor immune related genes. Based on these data, clinical testing of SAR439459 (TGF-β antibody) and Cemiplimab (anti-PD1) combination has recently been initiated in HCC patients (NCT03192345). Citation Format: Natalia Malkova, Joachim Theilhaber, Mikhail Levit, Tatiana Tolstykh, Andrew Hebert, Kevin Atchison, Adalis Maisonet, Sara Sara Sinicropi-Yao, Alexei Protopopov, Jack Pollard, Sukhvinder Sidhu, Tun Tun Lin, Lily Pao, Dmitri Wiederschain, Sharad K. Sharma. Pan-TGF-β inhibition with PD-1 blockade as a combination treatment strategy to augment anti-tumor immune response in hepatocellular carcinoma [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 1909.

The successful application of Next Generation Sequencing (NGS) to drug discovery requires systems to manage and document each step of the sequencing process from sample receipt through data generation and data processing. We combined BenchlingTM, a solution for tracking NGS lab processes, with FONDA (Framework Of Next generation sequencing Data Analysis) an internally developed data processing platform, to support multiple types of NGS data generation and processing. Benchling combines a digital notebook and a laboratory information management system (LIMS). The system documents and automates steps in the NGS process including: sample registration, nucleic acid extraction, library construction, flow cell construction, sequencer sample sheet generation and BCL2FASTQ conversion. This enables wet lab scientists to easily retrieve an appropriate protocol for each sample and sequencing library type. We connected our sequencers to Benchling in order to monitor each sequencing run and to keep track of the quality of NGS data. In addition, it generates “analysis ready sample sheet” (contains project and study information, location of FASTQ, sample species and library type) and uploads it into designated S3 buckets for data processing. Benchling dashboards provide overviews of NGS sample preparation, data generation and quality control. In summary, Benchling interconnects the original sample, the labels, the barcodes, the cDNA/DNA, the library, and all the QC results. We process NGS data using pipelines implemented in FONDA on a dockerized Amazon Web Services cloud platform. Analyses can be configured automatically from information exported by Benchling or launched manually. After data processing is completed, output files such as gene expression counts or variant calls are deposited into project-specific folders, ready for secondary analysis. In the current FONDA version (as of Nov 2020), we have developed pipelines for single cell multi-omics (CITE-seq and single-cell immune profiling) and bulk RNA-seq. The modular design of FONDA facilitates the development, the updating, and the extension of pipelines to new sequencing technologies. In summary, Benchling and FONDA enable high quality sample and NGS data flows from the lab for target identification, understanding mechanism of action, patient stratification and biomarker discovery. Availability and implementation: FONDA is implemented in Java and released under the Apache License 2.0. FONDA can be downloaded from GitHub at https://github.com/epam/fonda. Citation Format: Chandra Sekhar Pedamallu, Joon Sang Lee, Shu Yan, Adalis Maisonet, Aleksandr Sidoruk, Tengui Chen, Yulia Kamyshova, Mariia Zueva, Mark Magid, Quan Wan, Jeffrey Thompson, Valerie Zebrouck, Immanuel Gadaczek, Mikhail Alperovich, Brian McNatt, Alexei Protopopov, Donald Jackson, Jack Pollard. A comprehensive sample tracking and data processing workflow for next generation sequencing [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 2280.

Cancer immunotherapies with anti-PD-1/PD-L1 checkpoint blockade antibodies have revolutionized the treatment of a wide variety of malignancies. However, immunotherapy is ineffective in a significant subset of patients, or eventually results in the development of resistance with relapsed disease. Therefore, the future of immuno-oncology is identification of new agents that can be combined to increase the depth and breadth of anti-PD-1/PD-L1 therapies. Local intratumoral delivery of messenger RNA (mRNA)-based immunotherapies provides an opportunity to stimulate innate and adaptive immune responses against tumors and modulate tumor microenvironment, while limiting the side-effects related to systemic administration of immuno-modulatory therapeutics. We examined the local immunotherapy of synthetic mRNAs encoding immunomodulatory cytokines (single chain interleukin-12 (IL-12), interferon alpha (IFN-α), granulocyte-macrophage colony-stimulating factor (GM-CSF) and interleukin-15 sushi(IL-15)) alone and in combination with anti-PD-1 antibodies across 12 syngeneic mouse tumor models representing a diverse range of murine tumor types. Single agent treatment with cytokine mRNAs resulted in a significant anti-tumor response in 8 out of 12 tumor models. Combining cytokine mRNA with systemic anti-PD-1 immunotherapy further improved the anti-tumor response and resulted in tumor growth inhibition in two additional models otherwise unresponsive to single agent treatments. Transcriptome and cytokine analyses have been performed to understand factors which determine sensitivity to single agent and combination treatments with cytokine mRNA and anti-PD-1 antibodies. In summary, the combination of local cytokine mRNA immunotherapy with immune checkpoint blockade demonstrated improvement in the anti-tumor activity in a range of preclinical cancer models representing different murine tumor types. Based on these and other data, clinical testing of cytokine encoding mRNA mixture has recently been initiated (NCT03871348). Citation Format: Natalia V. Malkova, Tatiana Tolstykh, Mikhail Levit, Joachim Theilhaber, Andrew Hebert, Kevin Atchison, Adalis Maisonet, Christian Hotz, Ugur Sahin, Alexei Protopopov, Jack Pollard, Sukhvinder Sidhu, Dmitri Wiederschain, Timothy R. Wagenaar. Combination of local mRNA immunotherapy with systemic immune checkpoint blockade demonstrates anti-tumor activity across a diverse range of preclinical syngeneic tumor models [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 4451.

Abstract 472 We and others recently identified locus rearrangements involving the type I cytokine receptor subunit CRLF2 (also known as thymic stromal lymphopoietin receptor (TSLPR)) in 5–7% of all adult and pediatric B-cell precursor acute lymphoblastic leukemia (B-ALL). CRLF2 rearrangement places full-length CRLF2 under alternate transcriptional control, and can either result from an intrachromosomal CRLF2-P2RY8 deletion or from a CRLF2-IGH translocation. Prognosis for CRLF2-rearranged B-ALL is particularly poor, suggesting that a sizable fraction of relapsed and ultimately fatal B-ALL harbor CRLF2 rearrangements. Approximately 50% of CRLF2-rearranged B-ALL harbor mutations in JAK2 (and rarely JAK1) that cluster around JAK2 Arg683. A separate 15% have a CRLF2 F232C mutation that promotes constitutive homodimerization and signaling. In the remaining cases, the driver of CRLF2 signaling is not known. To identify additional genetic alterations that contribute to CRLF2-mediated leukemogenesis, we performed next-generation sequencing on 3 CRLF2-rearranged B-ALL specimens, including one with CRLF2 F232C (#536). Exome libraries were assembled from bone marrow specimens with greater than 90% B-ALL involvement and from paired remission bone marrows. Transcriptome sequencing was also performed on cDNA isolated from the involved marrow of patient #536 to: 1) confirm the findings from exome sequencing, 2) focus on transcribed genes and 3) identify possible RNA editing events. Sequencing utilized the SOLiD (Sequencing by Oligonucleotide Ligation and Detection; Applied BioSystems) platform, with a target recovery of greater than 40 million (transcriptome) and 25 million (exome) uniquely mapping reads per sample. Mutations were considered high confidence if a minimum of 3 individual reads identified the same mutation within the involved specimen and the mutation was not identified in any reads from the remission specimen (minimum 10 reads at that base-pair). Paired exome sequencing of germline and involved marrow specimens identified 129, 297 and 630 high-confidence, non-synonymous, somatic mutations in the three samples. Of the total 1056 mutations, 1023 (96.9%) were missense and the remaining 33 (3.1%) were nonsense mutations. The expected mutation in CRLF2 F232C (#536) was recovered, as was a novel JAK2 mutation from a sample previously thought to be JAK2 wild-type (#002). Comparison between transcriptome and exome sequence markedly reduced the number of potential “driver” alterations, i.e., nonsynonymous coding mutations expressed in the tumor but not present in the remission exome. For example, from the 129 somatic alterations in the involved exome from #536, only 15 were recovered from the involved transcriptome. Sequence alterations affected genes known to be involved in histone modification, oxygen metabolism, and steroid responsiveness. To our knowledge, none of the identified mutations have previously been described. One mutation in a gene involved in E2F transcription was observed in two of three cases. In addition, 2,428 coding sequence mutations were identified from the involved marrow transcriptome of #536 that were not present in the involved marrow exome, possibly indicating a high rate of RNA editing. Additional sequence analysis and molecular epidemiology of the novel sequence alterations in CRLF2-rearranged and —unrearranged B-ALL is underway. Disclosures: No relevant conflicts of interest to declare.