Dimitrova, Nevenka

Abstract Copy Number Alterations (CNAs), including chromosomal gains and losses, shape the genetics and clinical management of acute myeloid leukemia (AML), particularly in complex karyotypes (e.g. CK-AML). Traditionally, AML associated CNAs have been studied using cytogenetic approaches, i.e., karyotyping, that are limited in resolution and sensitivity. Here, low-coverage, sparse whole genome sequencing followed by machine learning applied to over 600 AML patient samples (ECOG-ARCIN trials: E1900, E3999) reveals novel genetic, biologic, and clinical correlates in AML disease. We find that multi-variate Cox-regression analysis applied to the data identifies select patients exhibiting focal and cryptic hemizygous deletions at 17q, 21q, and 3p, in otherwise diploid genomes, associate with inferior survival compared to other patients (median overall survival; OS = 65.5 compared to 421 days, Chi-square test pval=6x10-11), independent of factors such as age, treatment, and karyotype complexity status. In addition, we confirm that amplifications at 11q23, encoding the KM2TA gene, are associated with inferior outcome, and further identify unique breakpoint event features as well as transcriptional signatures suggestive of distinctive KM2TA-translocation independent biology. Applying machine learning approaches to the data, more specifically Non-negative Matrix Factorization (NMF), we develop a prognostic model that captures clinically adverse, cryptic variation such as focal 5q deletions, missed by conventional chromosome analysis and thereby, adds novel, complementary information to traditional, cytogenetic based risk-stratification (e.g. ELN). The model also associates gain of chromosome 8 in Intermediate risk patients with worse outcome compared to other Intermediate risk patients (median OS = 186 compared to 446 days, Chi-square test pval=0.005) and identifies a subgroup of patients within the adverse risk category that lack deletions on chromosomes 5, 7, and 17, and that fare better than the rest of the adverse risk patients (median OS = 188 compared to 97.5 days, Chi-square test pval=0.02), likely reflecting previously identified “atypical” CK-AML patients. Last, we develop a prognostic classifier based on a combination of Neural-Network and Random-Forest based learning, that predicts disease outcome with high accuracy and is based on a three genomic feature list on chromosome bands 5q31.2, 7q31.33, and 8q24, suggesting potential for clinical implementation. Altogether, our results show that low coverage, sparse whole genome sequencing combined with advanced learning methods can be applied clinically to support the diagnosis and clinical management of AML disease as well as serve as a powerful tool to further a basic understanding of the genetics and biology of CNAs in myeloid neoplasms and other cancers.

Abstract The goal of this study is to evaluate the potential use of gene expression profile of peripheral blood mononuclear cells (PBMCs) as a non-invasive biomarker for predicting the response of anti-PD-1 therapy in non-small cell lung cancer (NSCLC) subjects. Peripheral blood of NSCLC stage 4 subjects treated with an anti-PD-1 monoclonal antibody was collected prior to treatment (week 0) and subsequent infusions. Total RNA was extracted, and libraries were prepared using TruSeq Stranded mRNA LT Sample Prep Kit and sequenced by Illumina NextSeq. The paired-end reads were mapped to human reference hg38 using STAR and gene expressions were calculated using RSEM. Preliminary analysis of differential gene expression (DE) was performed among the responders at respectively (T1) weeks 3-4, (T2) weeks 6-9 and (T3) weeks 12-16 after treatment, compared with the baseline at T0. Further DE analysis was performed between the baseline samples of responders and non-responders. Genes with FDR-adjusted p values less than 0.05 were selected for pathway enrichment analysis with DAVID. Altogether 30 RNA-Seq samples of seven subjects were collected at multiple time points before and after treatment. According to standard RECIST 1.1 criteria, five subjects had a partial response to the therapy, whereas two subjects had stable disease or no response. In addition, liquid biopsy results were available for selected samples. Among the responders, there are 203 DE genes for T1 with log2 fold change (LFC) of gene expression from -2.4 to 3.5, 859 DE genes for T2 with LFC from -3.4 to 3.3, and 395 DE genes for T3 with LFC from -3 to 6.8. Among the DE genes for the three time points, 155 genes are common to T1/T2, 250 common to T2/T3, and 104 common to T1/T2/T3. Pathway enrichment test using Gene Ontology Biological Process terms shows that the strongest DE pathways for initial response after treatment at T1 are regulation of immune response (FDR adjusted p = 2.5E-5), oxygen transport (p = 5.5E-3) and T cell costimulation (p = 3.9E-3). Based on the 104 common DE genes, oxygen transport (p = 0.029) is the only pathway consistently perturbed due to an increase in hemoglobin. When comparing the baseline of responders and non-responders, we identified a candidate gene signature, with some of the genes also reported in other studies. However, more samples are required to establish its statistical significance. Systemic immune response after first dose of anti-PD1 treatment is reflected by DE genes in blood samples collected in weeks 3-4. Tumor regression might be indicated by the consistent increase in hemoglobin level. The results of these analyses show promise of developing a predictive signature for the response of anti-PD-1 immunotherapy in NSCLC subjects based on the gene expression profile of PBMCs. While these are preliminary results, we continue to recruit subjects, and will present the up-to-date results at the conference. Citation Format: Yee Him Cheung, Jie Wu, Weihua Huang, Changhong Yin, Katherine Linder, Kaushal Parikh, Ke Tang, Christopher Kasbek, Jun Huang, Zeil Rosenberg, Michael Fanucchi, John Fallon, Nevenka Dimitrova. Differential gene expression analysis reveals pathways involved in anti-pd-1 therapy response prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4994.