Warren Tom

Spatial and temporal diversity in genomic instability processes defines lung cancer evolution

Spatial and temporal dissection of the genomic changes occurring during the evolution of human non–small cell lung cancer (NSCLC) may help elucidate the basis for its dismal prognosis. We sequenced 25 spatially distinct regions from seven operable NSCLCs and found evidence of branched evolution, with driver mutations arising before and after subclonal diversification. There was pronounced intratumor heterogeneity in copy number alterations, translocations, and mutations associated with APOBEC cytidine deaminase activity. Despite maintained carcinogen exposure, tumors from smokers showed a relative decrease in smoking-related mutations over time, accompanied by an increase in APOBEC-associated mutations. In tumors from former smokers, genome-doubling occurred within a smoking-signature context before subclonal diversification, which suggested that a long period of tumor latency had preceded clinical detection. The regionally separated driver mutations, coupled with the relentless and heterogeneous nature of the genome instability processes, are likely to confound treatment success in NSCLC. Different regions of a human lung tumor harbor different mutations, possibly explaining why the disease is so tough to treat. [Also see Perspective by Govindan] Space, time, and the lung cancer genome Lung cancer poses a formidable challenge to clinical oncologists. It is often detected at a late stage, and most therapies work for only a short time before the tumors resume their relentless growth. Two independent analyses of the human lung cancer genome may help explain why this disease is so resilient (see the Perspective by Govindan). Rather than take a single “snapshot” of the cancer genome, de Bruin et al. and Zhang et al. identified genomic alterations in spatially distinct regions of single lung tumors and used this information to infer the tumors evolutionary history. Each tumor showed tremendous spatial and temporal diversity in its mutational profiles. Thus, the efficacy of drugs may be short-lived because they destroy only a portion of the tumor. Science, this issue p. 251, p. 256; see also p. 169

Abstract B17: Validation of targeted gene expression profiling panel for identifying biomarker signatures of immunotherapy responders

Cancer immunotherapy has led to an unprecedented, long-lasting response in populations susceptible to the therapies. Despite the therapeutic potential, identifying biomarkers and stratifying populations that are likely to respond has been a challenge. Gene expression profiling has previously been successfully used to stratify individuals based on survival and treatment characteristics, but there exist limitations with the prevalent technologies. In particular, full transcriptome gene expression estimates use limited biological material to measure the concentrations of tens of thousands uninformative genes and often lack the depth required to accurately measure expression levels of lowly-expressed genes. These genes may be critical to the identification of a signature associated with immunotherapy responders. To efficiently measure the expression of the key genes potentially informative of an immunotherapy response, we developed a high-throughput targeted gene expression solution measured by our RNA Ion Oncomine Immune Response Profiling panel* containing 395 genes. This panel provides information about the expression of genes involved in tumor checkpoint inhibition (including CTLA4, PD-1, PD-L1, OX-40, 4-1BB, TIM3, LAG3) and other targets such as CSF1R, and IDO1, as well as additional markers of T cell signaling pathway, interferon signaling, and markers of tumor infiltrating lymphocytes (TIL). We used publicly available TCGA data to demonstrate the need and develop a solution for a new normalization procedure that allows for accurate comparisons of samples within various cancer types. Furthermore, we verified a linear and unbiased estimate of fold change in our assays across mixing concentrations of a cell-line titration experiment. Finally, by achieving a high correlation (r > .99) of technical replicates, along with robust expression estimation even at low input amounts (10 ng RNA), our panel offers a valuable solution for biomarker research in cancer immunotherapy. *For research use only. Not for use in diagnostic procedures. Citation Format: Aleksandr Pankov, Yongming Sun, Yuan-Chieh Ku, Warren Tom, Jianping Zheng, Timothy Looney, Janice Au-Yong, Fiona Hyland, Ann Mongan. Validation of targeted gene expression profiling panel for identifying biomarker signatures of immunotherapy responders. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2016 Oct 20-23; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2017;5(3 Suppl):Abstract nr B17.

Abstract 5364: A targeted NGS solution to evaluate gene expression signature of the tumor microenvironment from 40 NSCLC FFPE and matched fresh frozen samples

Cancer cells and their surrounding non-malignant cells, including immune cells, signaling molecules, stromal and extracellular matrix, create the tumor microenvironment (TME). The composition of this TME plays important roles in tumor progression, evading growth suppressors and activating metastasis. However, the regulatory mechanism and function of each constituent remains poorly understood. With several checkpoint blockade therapy studies, the presence of PD-L1 has been reported to be a promising marker to predict positive response. Current IHC methods to measure PD-L1 are subjective and highly variable. A higher-throughput and standardized solution that can systematically measure gene expression of cells present in the TME has emerged to be a more desirable alternative. Here, we applied the OncomineTM Immune Response Research Assay to measure the expression of 395 genes in non-small cell lung cancer (NSCLC) samples from 40 matched FFPE and fresh frozen sample types. This assay leverages NGS technology to sequence and count reads derived from the original transcript. With an input requirement of 10 ng of total RNA, libraries were generated, templated on the Ion ChefTM and sequenced on the Ion S5TM System. Results showed that, despite small input amount, the expression profiles of FFPE and fresh frozen samples are highly correlated with an average correlation greater than 0.9. We selected 22 genes out of the panel to validate expression with qPCR using FFPE samples. These genes were selected to cover a range of low, medium, and high expressors per our NGS data. Again, we observed a strong correlation (R ~ 0.9) between NGS and qPCR data. Approximately 80% of the 40 samples show moderate to high expression of CD8+ T cell cytokines, IFNG and TNFa. We further found that the expression of CD8A and CD8B are highly correlated with CD4, suggesting the co-presence of both cytotoxic and helper T cells. High correlation among CD4, FOXP3, TGFB1, and IL2RA (CD25) also suggests that their expression can be used as markers for the presence of Treg cells. We conducted a differential expression analysis between a group of samples (n=8) with high percentage of surrounding and infiltrating lymphocytes and another group (n=5) with low stromal content but devoid of infiltrating lymphocytes. Interestingly, we found a large number of genes which annotated as markers for infiltrating lymphocytes (CTSS, CXCR4, CD37, SRGN, FCER1G, SAMHD1, and GZMA) are significantly up-regulated in samples with high percentage of surrounding and infiltrating lymphocytes. In summary, this study highlights the robustness of using a targeted panel to understand the composition and regulatory mechanism of the TME and tumor immune response. Citation Format: Yuan-Chieh Ku, Warren Tom, Yongming Sun, Alex Pankov, Tim Looney, Fiona Hyland, Janice Au-Young, Ann Mongan. A targeted NGS solution to evaluate gene expression signature of the tumor microenvironment from 40 NSCLC FFPE and matched fresh frozen samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5364. doi:10.1158/1538-7445.AM2017-5364

Abstract 4751: Simple modifications in Ion Torrent Variant Caller (TVC) parameters improve somatic variant detection from circulating tumor cells and cell free DNA to 1% allele frequency

By increasing quality-associated parameters and lowering sensitivity thresholds, a significant improvement in low frequency allele detection was achieved. The approach described was optimized using a dilution series of synthetic spike-ins (the AcroMetrix® Oncology Hotspot Control), and a titrated range of tumor cell lines in a normal background. The optimized variant calling approach was then applied to the analysis of somatic variants in circulating tumor cell (CTC) samples and associated cell-free DNA (cfDNA) using next-generation sequencing (NGS). Libraries were produced using the Ion AmpliSeq™ Cancer Hotspot Panel v2 for coverage of 50 cancer-associated genes and 2,800 COSMIC mutations. Using the above samples, the improved somatic variant detection workflow demonstrated analytical sensitivity down to 1% allele ratio in the observed sequence reads. Citation Format: Colin Davidson, Yongming Sun, Christopher Davies, Chaitali Parikh, Warren Tom, Fiona Hyland, Charles Scafe, Dalia Dhingra. Simple modifications in Ion Torrent Variant Caller (TVC) parameters improve somatic variant detection from circulating tumor cells and cell free DNA to 1% allele frequency. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4751. doi:10.1158/1538-7445.AM2015-4751