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Supplementary Data from Integrative Profiling of T790M-Negative EGFR-Mutated NSCLC Reveals Pervasive Lineage Transition and Therapeutic Opportunities

Supplementary results and methods

Signaling between cancer and nonmalignant (stromal) cells in the tumor microenvironment (TME) is a key to tumor progression. Here, we deconvoluted bulk tumor transcriptomes to infer cross-talk between ligands and receptors on cancer and stromal cells in the TME of 20 solid tumor types. This approach recovered known transcriptional hallmarks of cancer and stromal cells and was concordant with single-cell, in situ hybridization and IHC data. Inferred autocrine cancer cell interactions varied between tissues but often converged on Ephrin, BMP, and FGFR-signaling pathways. Analysis of immune checkpoints nominated interactions with high levels of cancer-to-immune cross-talk across distinct tumor types. Strikingly, PD-L1 was found to be highly expressed in stromal rather than cancer cells. Overall, our study presents a new resource for hypothesis generation and exploration of cross-talk in the TME.Significance:This study provides deconvoluted bulk tumor transcriptomes across multiple cancer types to infer cross-talk in the tumor microenvironment.

Purpose:Despite the established role of EGFR tyrosine kinase inhibitors (TKIs) in EGFR-mutated NSCLC, drug resistance inevitably ensues, with a paucity of treatment options especially in EGFRT790M-negative resistance.Experimental Design:We performed whole-exome and transcriptome analysis of 59 patients with first- and second-generation EGFR TKI-resistant metastatic EGFR-mutated NSCLC to characterize and compare molecular alterations mediating resistance in T790M-positive (T790M+) and -negative (T790M−) disease.Results:Transcriptomic analysis revealed ubiquitous loss of adenocarcinoma lineage gene expression in T790M− tumors, orthogonally validated using multiplex IHC. There was enrichment of genomic features such as TP53 alterations, 3q chromosomal amplifications, whole-genome doubling and nonaging mutational signatures in T790M− tumors. Almost half of resistant tumors were further classified as immunehot, with clinical outcomes conditional on immune cell-infiltration state and T790M status. Finally, using a Bayesian statistical approach, we explored how T790M− and T790M+ disease might be predicted using comprehensive genomic and transcriptomic profiles of treatment-naïve patients.Conclusions:Our results illustrate the interplay between genetic alterations, cell lineage plasticity, and immune microenvironment in shaping divergent TKI resistance and outcome trajectories in EGFR-mutated NSCLC. Genomic and transcriptomic profiling may facilitate the design of bespoke therapeutic approaches tailored to a tumor's adaptive potential.

PURPOSE HER2-altered non–small-cell lung cancer (NSCLC) represents a diverse subgroup, including mutations, amplifications, and overexpression. However, HER2 exon 20 insertion mutations are emerging as a distinct molecular subtype with expanding therapeutic options. We describe the molecular epidemiology and genomic features of HER2-altered NSCLC in an Asian tertiary cancer center. METHODS We identified patients with HER2-mutated NSCLC in our institutional database, collating clinicopathological features and treatment outcomes. The genomic landscape of human epidermal growth factor receptor 2 ( HER2)–mutated NSCLC was further evaluated using whole-exome sequencing (WES) data from combined local and publicly available data sets. HER2 amplification and overexpression as selection biomarkers in NSCLC were further interrogated using HER2 immunohistochemistry and correlations with WES and RNA sequencing data. RESULTS Among 1,252 patients with consecutive lung adenocarcinoma undergoing routine next-generation sequencing, the prevalence of HER2 mutations was 3.1%—exon 20 insertion mutations comprised 2.7%. We examined the clinicopathological features in 55 patients with HER2-mutated NSCLC comprising 40 exon 20 insertion and 15 nonexon 20 insertion mutations. The most common exon 20 insertion mutation was HER2Y772_A775dup in 30 (75%), followed by HER2G776delinsVC in five patients (13%). There were limited responses to HER2-directed therapies apart from trastuzumab-deruxtecan, and no responses were seen with immunotherapy monotherapy. Evaluating the genomics features of HER2 exon 20 insertion mutations using WES data revealed low tumor mutational burden (TMB), low incidence of cancer driver comutations, and a predominance of aging mutational signature—similar to EGFR-mutated tumors. In contrast, uncommon (or nonexon 20 insertion) HER2-mutated tumors resembled EGFR wild-type tumors with higher TMB, higher frequency of cancer driver comutations, and greater presence of smoking and APOBEC mutational signature. Finally, in evaluating HER2 immunohistochemistry in all lung adenocarcinoma, there was significant discordance comparing different scoring systems and poor correlation with HER2 RNA expression and HER2 amplification. CONCLUSION The incidence of HER2 mutations is 3.1% in East Asian nonsquamous NSCLC. HER2 exon 20 insertion–mutated tumors appear genomically distinct from uncommon (nonexon 20 insertion) HER2 mutations, the latter demonstrating higher TMB, co-occurring drivers, and predominant nonaging mutational signature. The therapeutic implications of the genomic and clinical features of HER2-mutated NSCLC warrant further investigation.

Accurate identification of somatic mutations is crucial for discovery and identification of driver mutations in cancer tumors. Here, we describe the updated Somatic Mutation calling method using a Random Forest (SMuRF2), an ensemble method that combines the predictions and auxiliary features from individual mutation callers using supervised machine learning. SMuRF2 provides an efficient workflow to predict both somatic point mutations (SNVs) and small insertions/deletions (indels) in cancer genomes and exomes. We describe the latest method and provide a detailed tutorial for running SMuRF2.

Tumors are complex masses composed of malignant and non-malignant cells. Variation in tumor purity (malignant cell fraction) can both confound integrative analysis and enable studies of tumor heterogeneity. Here we developed PUREE, which uses a weakly supervised learning approach to infer tumor purity from a tumor gene expression profile. PUREE was trained on gene expression data and genomic consensus purity estimates from approximately 8000 solid tumor samples. Using a linear model based on 170 input genes, PUREE predicted purity with high accuracy across distinct solid tumor types and generalized to tumor samples from unseen tumor types. PUREE input genes features were further validated using single-cell RNA-seq data from distinct tumor types. In a comprehensive benchmark, PUREE outperformed all existing transcriptome-based purity estimation approaches. We also show that the accuracy of a pan-cancer model is comparable to models optimized for individual tumor types, highlighting compositional properties of the tumor microenvironment conserved across tumor types. Overall, PUREE is a highly accurate and versatile method for estimating tumor purity and interrogating tumor heterogeneity from bulk tumor gene expression data. Citation Format: Egor Revkov, Ken W.-K. Sung, Anders J. Skanderup. Accurate pan-cancer tumor purity estimation from gene expression data [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 1941.

Background: Early detection of recurrence and monitoring of MRD post-surgery is critical for clinical decision-making to tailor adjuvant therapy. In early-stage NSCLC, circulating tumor DNA (ctDNA) detection is especially challenging, requiring highly sensitive and specific assays. Therefore, we used a WGS approach (MRDetect) for ultra-sensitive ctDNA detection in NSCLC patients (pts) undergoing curative surgery. Methods: We conducted a pilot study to evaluate the MRDetect approach in serial plasma samples (including pre-surgery, post-surgery and follow-up [f/u] timepoints) from resected stage IB-IIIA NSCLC pts. Pts underwent routine surveillance by computed tomography scans. ctDNA was extracted from ~1mL plasma. MRDetect uses WGS by a tumor-informed approach (sequencing coverage 40x for tumor, 20x for plasma DNA) combined with AI-based error suppression models (trained and calibrated with a non-cancer cohort, n=17) to increase the signal to noise ratio for precise ctDNA detection, and improve the accuracy of readouts especially for low tumor burden scenarios. The assay reports the detection and quantification of ctDNA burden in blood with a prognostic value for risk of recurrence. The ability of the assay to predict recurrence from a single sample, taken at the clinical landmark point (median 1.6 mths post-surgery, range 0.1-6.5) was evaluated. Results: Overall, 52 NSCLC pts were enrolled (n=88 plasma samples) with median clinical f/u of 32.6 mths (range 3.1-98.6). There were 43 pts with post-surgery landmark samples, with median age 62 years, 70% were male, 79% were adenocarcinoma and 49% were EGFR mutated. 26% were stage IB and 37% each were stage II and III. There were 15/18 (sensitivity 83%) pts with confirmed radiological recurrence in which MRDetect was positive, including 6/7 (86%) EGFR mutated pts. The median RFS in MRDetect positive pts was 15.2 mths (range 3.7-33.4). Among 25 pts with no recurrence (median f/u 25.6 mths), MRDetect reported 4 pts to be MRD positive (specificity 84%). These results were consistent between EGFR mutated (sensitivity 86%, specificity 86%) and wildtype pts (sensitivity 82%, specificity 82%). For longitudinal samples (n=17 pts), negative ctDNA was associated with absence of recurrence in 14/15 pts (specificity 93%). At the AACR meeting, results from a planned larger validation study will be presented. Conclusion: Using a robust WGS implemented AI-based computational platform (MRDetect), we demonstrate high sensitivity and specificity detection of MRD in both EGFR mutated and wildtype NSCLC. With an increasing number of therapeutic options in the adjuvant setting for NSCLC, an ultra-sensitive MRD assay has the potential to facilitate personalized clinical decision-making for tailoring both the need and choice of adjuvant therapies. Citation Format: Aaron C. Tan, Stephanie P. Saw, Gillianne G. Lai, Kevin L. Chua, Angela Takano, Boon-Hean Ong, Tina P. Koh, Amit Jain, Wan Ling Tan, Quan Sing Ng, Ravindran Kanesvaran, Tanujaa Rajasekaran, Sunil Deochand, Dillon Maloney, Danielle Afterman, Tomer Lauterman, Noah Friedman, Imane Bourzgui, Nidhi Ramaraj, Zohar Donenhirsh, Ronel Veksler, Jonathan Rosenfeld, Ravi Kandasamy, Iman Tavassoly, Boris Oklander, Asaf Zviran, Wan-Teck Lim, Eng-Huat Tan, Anders J. Skanderup, Mei-Kim Ang, Daniel S. Tan. Ultra-sensitive detection of minimal residual disease (MRD) through whole genome sequencing (WGS) using an AI-based error suppression model in resected early-stage non-small cell lung cancer (NSCLC) [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 5114.

Genomic structural variations (SVs) causing rewiring ofWe applied PeNChIP-seq to 11 primary GCs and matched normal tissues combined with WGS profiles of200 GCs. Epigenome profiles were analysed alongside matched RNA-seq data to identify tumour-associated enhancer-based SVs with altered cancer transcription. Functional validation of candidate enhancer-based SVs was performed using CRISPR/Cas9 genome editing, chromosome conformation capture assays (4C-seq, Capture-C) and Hi-C analysis of primary GCs.PeNChIP-seq/WGS revealed ~150 enhancer-based SVs in GC. The majority (63%) of SVs linked to target gene deregulation were associated with increased tumour expression. Enhancer-based SVs targetingIntegrated paired-end NanoChIP-seq and WGS of gastric tumours reveals tumour-associated regulatory SV in regions associated with both simple and complex genomic rearrangements. Genomic rearrangements may thus exploit enhancer-hijacking as a common mechanism to drive oncogene expression in GC.