Small cell lung cancer (SCLC) is characterized by morphologic, epigenetic and transcriptomic heterogeneity. Subtypes based upon predominant transcription factor expression have been defined that, in mouse models and cell lines, exhibit potential differential therapeutic vulnerabilities, with epigenetically distinct SCLC subtypes also described. The clinical relevance of these subtypes is unclear, due in part to challenges in obtaining tumor biopsies for reliable profiling. Here we describe a robust workflow for genome-wide DNA methylation profiling applied to both patient-derived models and to patients’ circulating cell-free DNA (cfDNA). Tumor-specific methylation patterns were readily detected in cfDNA samples from patients with SCLC and were correlated with survival outcomes. cfDNA methylation also discriminated between the transcription factor SCLC subtypes, a precedent for a liquid biopsy cfDNA-methylation approach to molecularly subtype SCLC. Our data reveal the potential clinical utility of cfDNA methylation profiling as a universally applicable liquid biopsy approach for the sensitive detection, monitoring and molecular subtyping of patients with SCLC.

The growth factor TGFβ and the mechanosensitive calcium-permeable cation channel TRPV4 are both important for the development and maintenance of many tissues. Although TRPV4 and TGFβ both affect core cellular functions, how their signals are integrated is unknown. Here we show that pharmacological activation of TRPV4 significantly increased the canonical response to TGFβ stimulation in chondrocytes. Critically, this increase was only observed when TRPV4 was activated after, but not before TGFβ stimulation. The increase was prevented by pharmacological TRPV4 inhibition or knockdown and is calcium/CamKII dependent. RNA-seq analysis after TRPV4 activation showed enrichment for the TGFβ signalling pathway and identified JUN and SP1 as key transcription factors involved in this response. TRPV4 modulation of TGFβ signalling represents an important pathway linking mechanical signalling to tissue development and homeostasis.

Liquid biopsies and cell-free DNA (cfDNA) offer minimally invasive methods for the diagnosis and monitoring of Ewing Sarcoma (EwS). EwS have a low tumour mutational burden and their detection with plasma cfDNA is challenging. We hypothesised that analysing the cfDNA methylome and fragmentome could enhance sensitivity for detecting EwS and identifying disease recurrence. Using T7-MBD-seq, we conducted whole-genome and methylome sequencing of cfDNA from 87 serial samples of 23 patients with EwS and 3 patients with CIC-rearranged sarcoma (CIC). With EwingSign, a new machine learning model, we identified EwS or CIC in a test set for 11 out of 16 patients at diagnosis and 15 out of 18 clinically confirmed relapse events. 0 out of 24 non-cancer controls (NCC) were detected positive with EwingSign. When combined with global and regional fragmentome analysis, all 18 relapse cases were detected, with 15/18 detected by 2 or more modalities, and 1 out of 24 NCC was detected by one modality. These findings indicate that cfDNA methylome and fragmentome analysis, if validated in a larger cohort, could improve disease detection, monitoring and relapse identification in patients with EwS.

Cancers of Unknown Primary (CUP) comprise a heterogeneous group of metastatic cancers where the primary site is undetectable. It accounts for 2% of all new UK cancer diagnoses and is the 6^th leading cause of cancer death. Favourable subsets of CUP exist, based on clinico-pathological features, and although these patients have improved clinical outcomes, they make up only 20% of patients with CUP. The evolution of molecular profiling and tissue-of-origin (TOO) classifiers has the potential to transform diagnosis and management of patients with CUP. Reliable molecular profiling requires good quality and quantity tumour tissue material, often scarce in patients with CUP. Liquid biopsies offer an alternative, minimally invasive approach. We developed a novel cell-free DNA (cfDNA)-based multi-omics assay combining methylation profiling with mutation and copy number alterations (CNA) detection, to enable TOO prediction and biomarker discovery to aid treatment stratification. Methods: Two novel library preparation methods were applied to 41 CUP cfDNA samples, 80 non-cancer controls and 80 cfDNA samples from known tumour types (breast, lung, colorectal cancer): 1) Targeted (641 gene panel) next generation sequencing (NGS); 2) A multiplex genome-wide methylation capture assay (T7-MBDSeq). Both sequencing outputs can estimate CNA. Cancer Genome Interpreter (CGI) was used to evaluate the potential actionability of molecular alterations detected. A novel TOO classifier, generated utilising in silico dilution mixture sets derived from The Cancer Genome Atlas (TCGA) methylation data, demonstrated an AUROC of 0.96 across 15 different cancer groups. This was applied to cfDNA methylation data from known tumour types to test performance accuracy and subsequently applied to CUP cfDNA samples. Results: Multi-omics profiling from cfDNA was completed for 34/41 patients with CUP. cfDNA mutation profiling and estimated CNA analysis so far revealed >350 alterations across 225 genes. 17/34 (50%) of patients harboured potentially actionable alterations by CGI and, where performed, 68% of tumour mutations were concordant in the cfDNA. cfDNA methylation profiling revealed tumour-specific methylation changes in breast, lung and colorectal cancers. Our preliminary TOO classifier performance in these tumour types had an overall sensitivity and specificity of 85% and 98%, respectively. In patients with CUP, a predicted TOO was made in 26/41 (63%) patients and for 6 patients, where a subsequent primary tumour diagnosis was made, the TOO prediction was correct. Conclusions: cfDNA multi-omics profiling revealed potential therapeutic benefit in 77% of this retrospective CUP cohort and illustrates the potential to re-classify patients to primary tumour types where alternative therapies may improve clinical outcomes. Further multi-omics cfDNA profiling of the CUP cohort and validation in known primary cancer types are ongoing. Citation Format: Alicia-Marie Conway, Alexandra Clipson, Francesca Chemi, Simon Pearce, Katarzyna Kamieniecka, Saba Ferdous, Sophie Richardson, Elaine Kilgour, Matthew G. Krebs, Alastair Kerr, Dominic Rothwell, Natalie Cook, Caroline Dive. cfDNA multi-omics profiling and tissue of origin predictions in cancers of unknown primary [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 2808.

Summary: Liquid biopsies and cell-free DNA (cfDNA) offer minimally invasive methods for the diagnosis and monitoring of Ewing Sarcoma (EwS). EwS have a low tumour mutational burden and their detection with plasma cfDNA is challenging. We hypothesised that analysing the cfDNA methylome and fragmentome could enhance sensitivity for detecting EwS and identifying early disease recurrence. We conducted whole-genome and methylome sequencing of cfDNA from 68 serial samples of 15 patients with EwS and 3 patients with CIC-rearranged sarcoma (CIC). With EwingSign, a new machine learning model, we identified EwS or CIC in a test set for 10 out of 12 patients at diagnosis and 15 out of 18 clinically confirmed relapse events. 0 out of 29 non-cancer controls were detected positive with EwingSign. These findings indicate that cfDNA methylome and fragmentome analysis, if validated in a larger cohort, could improve disease detection, monitoring and relapse identification in patients with EwS.