Mechanistic Characterization of Cancer Associated Fibroblast Depletion via an Antibody-Drug Conjugate Targeting Fibroblast Activation Protein.

Cancer-associated fibroblasts (CAFs) are a prominent cell type within the tumor microenvironment where they are known to promote cancer cell growth and survival, angiogenesis, drug resistance, and immunosuppression. The transmembrane prolyl protease Fibroblast Activation Protein (FAP) is expressed on the surface of highly pro-tumorigenic CAFs found in the stroma of nearly every cancer of epithelial origin. The widespread expression of FAP has made it an attractive therapeutic target based on the underlying hypothesis that eliminating pro-tumorigenic CAFs will disrupt the crosstalk between components of TME resulting in cancer cell death and immune infiltration. This hypothesis, however, has never been directly proven. To eliminate FAP-expressing CAFs, we developed an antibody-drug conjugate (ADC) using our anti-FAP antibody, huB12, coupled to a monomethyl auristatin E (huB12-MMAE) payload. After determining that huB12 was an effective targeting vector, we found that huB12-MMAE potently eliminated FAP-expressing cells as monocultures in vitro and significantly prolonged survival in vivo using a xenograft engineered to overexpress FAP. We investigated the effects of selectively eliminating CAFs using a layered, open microfluidic cell co-culture platform, known as the Stacks. Analysis of mRNA and protein expression found that treatment with huB12-MMAE resulted in the increased secretion of the pro-inflammatory cytokines IL6 and IL8 by CAFs and an associated increase in expression of pro-inflammatory genes in cancer cells. We also detected increased secretion of CSF1, a cytokine involved in myeloid recruitment and differentiation. Our findings suggest that the mechanism of FAP-targeted therapies are through effects on the immune microenvironment and anti-tumor immune response.

Prostate Cancer, Version 3.2024.

The NCCN Guidelines for Prostate Cancer include recommendations for staging and risk assessment after a prostate cancer diagnosis and for the care of patients with localized, regional, recurrent, and metastatic disease. These NCCN Guidelines Insights summarize the panel's discussions for the 2024 update to the guidelines with regard to initial risk stratification, initial management of very-low-risk disease, and the treatment of nonmetastatic recurrence.

A platform-independent AI tumor lineage and site (ATLAS) classifier.

Histopathologic diagnosis and classification of cancer plays a critical role in guiding treatment. Advances in next-generation sequencing have ushered in new complementary molecular frameworks. However, existing approaches do not independently assess both site-of-origin (e.g. prostate) and lineage (e.g. adenocarcinoma) and have minimal validation in metastatic disease, where classification is more difficult. Utilizing gradient-boosted machine learning, we developed ATLAS, a pair of separate AI Tumor Lineage and Site-of-origin models from RNA expression data on 8249 tumor samples. We assessed performance independently in 10,376 total tumor samples, including 1490 metastatic samples, achieving an accuracy of 91.4% for cancer site-of-origin and 97.1% for cancer lineage. High confidence predictions (encompassing the majority of cases) were accurate 98-99% of the time in both localized and remarkably even in metastatic samples. We also identified emergent properties of our lineage scores for tumor types on which the model was never trained (zero-shot learning). Adenocarcinoma/sarcoma lineage scores differentiated epithelioid from biphasic/sarcomatoid mesothelioma. Also, predicted lineage de-differentiation identified neuroendocrine/small cell tumors and was associated with poor outcomes across tumor types. Our platform-independent single-sample approach can be easily translated to existing RNA-seq platforms. ATLAS can complement and guide traditional histopathologic assessment in challenging situations and tumors of unknown primary.

Combining Dual Checkpoint Immunotherapy with Ablative Radiation to All Sites of Oligometastatic Non-Small Cell Lung Cancer: Toxicity and Efficacy Results of a Phase 1b Trial.

PURPOSE: Ablative local treatment of all radiographically detected metastatic sites in patients with oligometastatic non-small cell lung cancer (NSCLC) increases progression-free survival (PFS) and overall survival (OS). Prior studies demonstrated the safety of combining stereotactic body radiation therapy (SBRT) with single-agent immunotherapy. We investigated the safety of combining SBRT to all metastatic tumor sites with dual checkpoint, anticytotoxic T-lymphocyte-associated protein 4 (anti-CTLA-4), and anti-programmed cell death ligand 1 (anti-PD-L1) immunotherapy for patients with oligometastatic NSCLC. METHODS AND MATERIALS: We conducted a phase 1b clinical trial in patients with oligometastatic NSCLC with up to 6 sites of extracranial metastatic disease. All sites of disease were treated with SBRT to a dose of 30 to 50 Gy in 5 fractions. Dual checkpoint immunotherapy was started 7 days after completion of radiation using anti-CTLA-4 (tremelimumab) and anti-PD-L1 (durvalumab) immunotherapy for a total of 4 cycles followed by durvalumab alone until progression or toxicity. RESULTS: Of the 17 patients enrolled in this study, 15 patients received at least 1 dose of combination immunotherapy per protocol. The study was closed early (17 of planned 21 patients) due to slow accrual during the COVID-19 pandemic. Grade 3+ treatment-related adverse events were observed in 6 patients (40%), of which only one was possibly related to the addition of SBRT to immunotherapy. Median PFS was 42 months and median OS has not yet been reached. CONCLUSIONS: Delivering ablative SBRT to all sites of metastatic disease in combination with dual checkpoint immunotherapy did not result in excessive rates of toxicity compared with historical studies of dual checkpoint immunotherapy alone. Although the study was not powered for treatment efficacy results, durable PFS and OS results suggest potential therapeutic benefit compared with immunotherapy or radiation alone in this patient population.

Prostate Cancer, Version 4.2023, NCCN Clinical Practice Guidelines in Oncology.

The NCCN Guidelines for Prostate Cancer provide a framework on which to base decisions regarding the workup of patients with prostate cancer, risk stratification and management of localized disease, post-treatment monitoring, and treatment of recurrence and advanced disease. The Guidelines sections included in this article focus on the management of metastatic castration-sensitive disease, nonmetastatic castration-resistant prostate cancer (CRPC), and metastatic CRPC (mCRPC). Androgen deprivation therapy (ADT) with treatment intensification is strongly recommended for patients with metastatic castration-sensitive prostate cancer. For patients with nonmetastatic CRPC, ADT is continued with or without the addition of certain secondary hormone therapies depending on prostate-specific antigen doubling time. In the mCRPC setting, ADT is continued with the sequential addition of certain secondary hormone therapies, chemotherapies, immunotherapies, radiopharmaceuticals, and/or targeted therapies. The NCCN Prostate Cancer Panel emphasizes a shared decision-making approach in all disease settings based on patient preferences, prior treatment exposures, the presence or absence of visceral disease, symptoms, and potential side effects.

Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer.

Background: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life and there remain no validated predictive models to guide its use. Methods: Digital pathology image and clinical data from pre-treatment prostate tissue from 5,727 patients enrolled on five phase III randomized trials treated with radiotherapy +/- ADT were used to develop and validate an artificial intelligence (AI)-derived predictive model to assess ADT benefit with the primary endpoint of distant metastasis. After the model was locked, validation was performed on NRG/RTOG 9408 (n = 1,594) that randomized men to radiotherapy +/- 4 months of ADT. Fine-Gray regression and restricted mean survival times were used to assess the interaction between treatment and predictive model and within predictive model positive and negative subgroup treatment effects. Results: In the NRG/RTOG 9408 validation cohort (14.9 years of median follow-up), ADT significantly improved time to distant metastasis (subdistribution hazard ratio [sHR] = 0.64, 95%CI [0.45-0.90], p = 0.01). The predictive model-treatment interaction was significant (p-interaction = 0.01). In predictive model positive patients (n = 543, 34%), ADT significantly reduced the risk of distant metastasis compared to radiotherapy alone (sHR = 0.34, 95%CI [0.19-0.63], p < 0.001). There were no significant differences between treatment arms in the predictive model negative subgroup (n = 1,051, 66%; sHR = 0.92, 95%CI [0.59-1.43], p = 0.71). Conclusions: Our data, derived and validated from completed randomized phase III trials, show that an AI-based predictive model was able to identify prostate cancer patients, with predominately intermediate-risk disease, who are likely to benefit from short-term ADT.

PARP inhibitors in metastatic prostate cancer.

Poly-ADP ribose polymerase inhibitors (PARPi) are an emerging therapeutic option for the treatment of prostate cancer. Their primary mechanism of action is via induction of synthetic lethality in cells with underlying deficiencies in homologous recombination repair (HRR). In men with metastatic castrate-resistant prostate cancer (mCRPC) and select HRR pathway alterations, PARPi treatment has been shown to induce objective tumor responses as well as improve progression free and overall survival. Presently, there are two PARPi, olaparib and rucaparib, that are FDA approved in the treatment of mCRPC. Ongoing research is focused on identifying which HRR alterations are best suited to predict response to PARPi so that these therapies can be most effectively utilized in the clinic. While resistance to PARPi remains a concern, combination therapies may represent a mechanism to overcome or delay resistance.

Expression and Therapeutic Targeting of TROP-2 in Treatment-Resistant Prostate Cancer.

PURPOSE: Men with metastatic castration-resistant prostate cancer (mCRPC) frequently develop resistance to androgen receptor signaling inhibitor (ARSI) treatment; therefore, new therapies are needed. Trophoblastic cell-surface antigen (TROP-2) is a transmembrane protein identified in prostate cancer and overexpressed in multiple malignancies. TROP-2 is a therapeutic target for antibody-drug conjugates (ADC). EXPERIMENTAL DESIGN: TROP-2 gene (TACSTD2) expression and markers of treatment resistance from prostate biopsies were analyzed using data from four previously curated cohorts of mCRPC (n = 634) and the PROMOTE study (dbGaP accession phs001141.v1.p1, n = 88). EPCAM or TROP-2-positive circulating tumor cells (CTC) were captured from peripheral blood for comparison of protein (n = 15) and gene expression signatures of treatment resistance (n = 40). We assessed the efficacy of TROP-2-targeting agents in a mouse xenograft model generated from prostate cancer cell lines. RESULTS: We demonstrated that TACSTD2 is expressed in mCRPC from luminal and basal tumors but at lower levels in patients with neuroendocrine prostate cancer. Patients previously treated with ARSI showed no significant difference in TACSTD2 expression, whereas patients with detectable AR-V7 expression showed increased expression. We observed that TROP-2 can serve as a cell surface target for isolating CTCs, which may serve as a predictive biomarker for ADCs. We also demonstrated that prostate cancer cell line xenografts can be targeted specifically by labeled anti-TROP-2 agents in vivo. CONCLUSIONS: These results support further studies on TROP-2 as a therapeutic and diagnostic target for mCRPC.

ASSESSMENT OF CELL SURFACE TARGETS IN METASTATIC PROSTATE CANCER: EXPRESSION LANDSCAPE AND MOLECULAR CORRELATES.

Therapeutic approaches targeting proteins on the surface of cancer cells have emerged as an important strategy for precision oncology. To fully capitalize on the potential impact of drugs targeting surface proteins, detailed knowledge about the expression patterns of the target proteins in tumor tissues is required. In castration-resistant prostate cancer (CRPC), agents targeting prostate-specific membrane antigen (PSMA) have demonstrated clinical activity. However, PSMA expression is lost in a significant number of CRPC tumors, and the identification of additional cell surface targets is necessary in order to develop new therapeutic approaches. Here, we performed a comprehensive analysis of the expression and co-expression patterns of trophoblast cell-surface antigen 2 (TROP2), delta-like ligand 3 (DLL3), and carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) in CRPC samples from a rapid autopsy cohort. We show that DLL3 and CEACAM5 exhibit the highest expression in neuroendocrine prostate cancer (NEPC), while TROP2 is expressed across different CRPC molecular subtypes, except for NEPC. We observed variable intra-tumoral and inter-tumoral heterogeneity and no dominant metastatic site predilections for TROP2, DLL3, and CEACAM5. We further show that AR amplifications were associated with higher expression of PSMA and TROP2 but lower DLL3 and CEACAM5 levels. Conversely, PSMA and TROP2 expression was lower in RB1-altered tumors. In addition to genomic alterations, we demonstrate a tight correlation between epigenetic states, particularly histone H3 lysine 27 methylation (H3K27me3) at the transcriptional start site and gene body of TACSTD2 (encoding TROP2), DLL3, and CEACAM5, and their respective protein expression in CRPC patient-derived xenografts. Collectively, these findings provide novel insights into the patterns and determinants of expression of TROP2, DLL3, and CEACAM5 with important implications for the clinical development of cell surface targeting agents in CRPC.

Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer.

Predictive Model for Hormone Therapy in Prostate CancerDigital pathology images and clinical data from pretreatment prostate tissue were used to generate a predictive model to determine patients who would benefit from androgen deprivation therapy (ADT). In model-positive patients, ADT significantly reduced the risk of distant metastasis compared with radiotherapy alone.

NCCN Guidelines® Insights: Prostate Cancer, Version 1.2023.

The NCCN Guidelines for Prostate Cancer address staging and risk assessment after a prostate cancer diagnosis and include management options for localized, regional, recurrent, and metastatic disease. The NCCN Prostate Cancer Panel meets annually to reevaluate and update their recommendations based on new clinical data and input from within NCCN Member Institutions and from external entities. These NCCN Guidelines Insights summarizes much of the panel's discussions for the 4.2022 and 1.2023 updates to the guidelines regarding systemic therapy for metastatic prostate cancer.

A clinical-grade liquid biomarker detects neuroendocrine differentiation in prostate cancer.

BackgroundNeuroendocrine prostate cancer (NEPC) is an aggressive subtype, the presence of which changes the prognosis and management of metastatic prostate cancer.MethodsWe performed analytical validation of a Circulating Tumor Cell (CTC) multiplex RNA qPCR assay to identify the limit of quantification (LOQ) in cell lines, synthetic cDNA, and patient samples. We next profiled 116 longitudinal samples from a prospectively collected institutional cohort of 17 patients with metastatic prostate cancer (7 NEPC, 10 adenocarcinoma) as well as 265 samples from 139 patients enrolled in 3 adenocarcinoma phase II trials of androgen receptor signaling inhibitors (ARSIs). We assessed a NEPC liquid biomarker via the presence of neuroendocrine markers and the absence of androgen receptor (AR) target genes.ResultsUsing the analytical validation LOQ, liquid biomarker NEPC detection in the longitudinal cohort had a per-sample sensitivity of 51.35% and a specificity of 91.14%. However, when we incorporated the serial information from multiple liquid biopsies per patient, a unique aspect of this study, the per-patient predictions were 100% accurate, with a receiver-operating-curve (ROC) AUC of 1. In the adenocarcinoma ARSI trials, the presence of neuroendocrine markers, even while AR target gene expression was retained, was a strong negative prognostic factor.ConclusionOur analytically validated CTC biomarker can detect NEPC with high diagnostic accuracy when leveraging serial samples that are only feasible using liquid biopsies. Patients with expression of NE genes while retaining AR-target gene expression may indicate the transition to neuroendocrine differentiation, with clinical characteristics consistent with this phenotype.FundingNIH (DP2 OD030734, 1UH2CA260389, R01CA247479, and P30 CA014520), Department of Defense (PC190039 and PC200334), and Prostate Cancer Foundation (Movember Foundation - PCF Challenge Award).

Intrinsic Molecular Subtypes of Metastatic Castration-Resistant Prostate Cancer.

PURPOSE: Although numerous biology-driven subtypes have been described previously in metastatic castration-resistant prostate cancer (mCRPC), unsupervised molecular subtyping based on gene expression has been less studied, especially using large cohorts. Thus, we sought to identify the intrinsic molecular subtypes of mCRPC and assess molecular and clinical correlates in the largest combined cohort of mCRPC samples with gene expression data available to date. EXPERIMENTAL DESIGN: We combined and batch-effect corrected gene expression data from four mCRPC cohorts from the Fred Hutchinson Cancer Research Center (N = 157), a small-cell neuroendocrine (NE) prostate cancer (SCNC)-enriched cohort from Weill Cornell Medicine (N = 49), and cohorts from the Stand Up 2 Cancer/Prostate Cancer Foundation East Coast Dream Team (N = 266) and the West Coast Dream Team (N = 162). RESULTS: Hierarchical clustering of RNA-sequencing data from these 634 mCRPC samples identified two distinct adenocarcinoma subtypes, one of which (adeno-immune) was characterized by higher gene expression of immune pathways, higher CIBERSORTx immune scores, diminished ASI benefit, and non-lymph node metastasis tropism compared with an adeno-classic subtype. We also identified two distinct subtypes with enrichment for an NE phenotype, including an NE-liver subgroup characterized by liver metastasis tropism, PTEN loss, and APC and SPOP mutations compared with an NE-classic subgroup. CONCLUSIONS: Our results emphasize the heterogeneity of mCRPC beyond currently accepted molecular phenotypes, and suggest that future studies should consider incorporating transcriptome-wide profiling to better understand how these differences impact treatment responses and outcomes.

Identification of phenocopies improves prediction of targeted therapy response over DNA mutations alone.

DNA mutations in specific genes can confer preferential benefit from drugs targeting those genes. However, other molecular perturbations can "phenocopy" pathogenic mutations, but would not be identified using standard clinical sequencing, leading to missed opportunities for other patients to benefit from targeted treatments. We hypothesized that RNA phenocopy signatures of key cancer driver gene mutations could improve our ability to predict response to targeted therapies, despite not being directly trained on drug response. To test this, we built gene expression signatures in tissue samples for specific mutations and found that phenocopy signatures broadly increased accuracy of drug response predictions in-vitro compared to DNA mutation alone, and identified additional cancer cell lines that respond well with a positive/negative predictive value on par or better than DNA mutations. We further validated our results across four clinical cohorts. Our results suggest that routine RNA sequencing of tumors to identify phenocopies in addition to standard targeted DNA sequencing would improve our ability to accurately select patients for targeted therapies in the clinic.

The 5-Hydroxymethylcytosine Landscape of Prostate Cancer.

Analysis of DNA methylation is a valuable tool to understand disease progression and is increasingly being used to create diagnostic and prognostic clinical biomarkers. While conversion of cytosine to 5-methylcytosine (5mC) commonly results in transcriptional repression, further conversion to 5-hydroxymethylcytosine (5hmC) is associated with transcriptional activation. Here we perform the first study integrating whole-genome 5hmC with DNA, 5mC, and transcriptome sequencing in clinical samples of benign, localized, and advanced prostate cancer. 5hmC is shown to mark activation of cancer drivers and downstream targets. Furthermore, 5hmC sequencing revealed profoundly altered cell states throughout the disease course, characterized by increased proliferation, oncogenic signaling, dedifferentiation, and lineage plasticity to neuroendocrine and gastrointestinal lineages. Finally, 5hmC sequencing of cell-free DNA from patients with metastatic disease proved useful as a prognostic biomarker able to identify an aggressive subtype of prostate cancer using the genes TOP2A and EZH2, previously only detectable by transcriptomic analysis of solid tumor biopsies. Overall, these findings reveal that 5hmC marks epigenomic activation in prostate cancer and identify hallmarks of prostate cancer progression with potential as biomarkers of aggressive disease. SIGNIFICANCE: In prostate cancer, 5-hydroxymethylcytosine delineates oncogene activation and stage-specific cell states and can be analyzed in liquid biopsies to detect cancer phenotypes. See related article by Wu and Attard, p. 3880.

Reversible epigenetic alterations regulate class I HLA loss in prostate cancer.

Downregulation of HLA class I (HLA-I) impairs immune recognition and surveillance in prostate cancer and may underlie the ineffectiveness of checkpoint blockade. However, the molecular mechanisms regulating HLA-I loss in prostate cancer have not been fully explored. Here, we conducted a comprehensive analysis of HLA-I genomic, epigenomic and gene expression alterations in primary and metastatic human prostate cancer. Loss of HLA-I gene expression was associated with repressive chromatin states including DNA methylation, histone H3 tri-methylation at lysine 27, and reduced chromatin accessibility. Pharmacological DNA methyltransferase (DNMT) and histone deacetylase (HDAC) inhibition decreased DNA methylation and increased H3 lysine 27 acetylation and resulted in re-expression of HLA-I on the surface of tumor cells. Re-expression of HLA-I on LNCaP cells by DNMT and HDAC inhibition increased activation of co-cultured prostate specific membrane antigen (PSMA)27-38-specific CD8+ T-cells. HLA-I expression is epigenetically regulated by functionally reversible DNA methylation and chromatin modifications in human prostate cancer. Methylated HLA-I was detected in HLA-Ilow circulating tumor cells (CTCs), which may serve as a minimally invasive biomarker for identifying patients who would benefit from epigenetic targeted therapies.

A role for microfluidic systems in precision medicine.

Precision oncology continues to challenge the "one-size-fits-all" dogma. Under the precision oncology banner, cancer patients are screened for molecular tumor alterations that predict treatment response, ideally leading to optimal treatments. Functional assays that directly evaluate treatment efficacy on the patient's cells offer an alternative and complementary tool to improve the accuracy of precision oncology. Unfortunately, traditional Petri dish-based assays overlook much tumor complexity, limiting their potential as predictive functional biomarkers. Here, we review past applications of microfluidic systems for precision medicine and discuss the present and potential future role of functional microfluidic assays as treatment predictors.

Longitudinal Molecular Profiling of Circulating Tumor Cells in Metastatic Renal Cell Carcinoma.

PURPOSE: Liquid biopsies in metastatic renal cell carcinoma (mRCC) provide a unique approach to understand the molecular basis of treatment response and resistance. This is particularly important in the context of immunotherapies, which target key immune-tumor interactions. Unlike metastatic tissue biopsies, serial real-time profiling of mRCC is feasible with our noninvasive circulating tumor cell (CTC) approach. METHODS: We collected 457 longitudinal liquid biopsies from 104 patients with mRCC enrolled in one of two studies, either a prospective cohort or a phase II multicenter adaptive immunotherapy trial. Using a novel CTC capture and automated microscopy platform, we profiled CTC enumeration and expression of HLA I and programmed cell death-ligand 1 (PD-L1). Given their diametric immunological roles, we focused on the HLA I to PD-L1 ratio (HP ratio). RESULTS: Patients with radiographic responses showed significantly lower CTC abundances throughout treatment. Furthermore, patients whose CTC enumeration trajectory was in the highest quartile (> 0.12 CTCs/mL annually) had shorter overall survival (median 17.0 months v 21.1 months, P < .001). Throughout treatment, the HP ratio decreased in patients receiving immunotherapy but not in patients receiving tyrosine kinase inhibitors. Patients with an HP ratio trajectory in the highest quartile (≥ 0.0012 annually) displayed significantly shorter overall survival (median 18.4 months v 21.2 months, P = .003). CONCLUSION: In the first large longitudinal CTC study in mRCC to date to our knowledge, we identified the prognostic importance of CTC enumeration and the change over time of both CTC enumeration and the HP ratio. These insights into changes in both tumor burden and the molecular profile of tumor cells in response to different treatments provide potential biomarkers to predict and monitor response to immunotherapy in mRCC.

Analytical validation and initial clinical testing of quantitative microscopic evaluation for PD-L1 and HLA I expression on circulating tumor cells from patients with non-small cell lung cancer.

INTRODUCTION: PD-L1 expression in non-small cell lung cancer (NSCLC) predicts response to immune checkpoint blockade, however is an imperfect biomarker given tumor heterogeneity, and the antigen presentation pathway requiring other components including HLA I expression. HLA I downregulation may contribute to resistance, warranting its evaluation in attempts to guide patient selection. In addition, earlier detection of acquired resistance could prompt earlier change in treatment and prolong patient survival. Analysis of circulating tumor cells (CTCs) captures heterogeneity across multiple sites of metastases, enables detection of changes in tumor burden that precede radiographic response, and can be obtained in serial fashion. METHODS: To quantify the expression of both PD-L1 and HLA I on CTCs, we developed exclusion-based sample preparation technology, achieving high-yield with gentle magnetic movement of antibody-labeled cells through virtual barriers of surface tension. To achieve clinical-grade quantification of rare cells, we employ high quality fluorescence microscopy image acquisition and automated image analysis together termed quantitative microscopy. RESULTS: In preparation for clinical laboratory implementation, we demonstrate high precision and accuracy of these methodologies using a diverse set of control materials. Preliminary testing of CTCs isolated from patients with NSCLC demonstrate heterogeneity in PD-L1 and HLA I expression and promising clinical value in predicting PFS in response to PD-L1 targeted therapies. CONCLUSIONS: By confirming high performance, we ensure compatibility for clinical laboratory implementation and future application to better predict and detect resistance to PD-L1 targeted therapy in patients with NSCLC.

SEEMLIS: a flexible semi-automated method for enrichment of methylated DNA from low-input samples.

BACKGROUND: DNA methylation alterations have emerged as hallmarks of cancer and have been proposed as screening, prognostic, and predictive biomarkers. Traditional approaches for methylation analysis have relied on bisulfite conversion of DNA, which can damage DNA and is not suitable for targeted gene analysis in low-input samples. Here, we have adapted methyl-CpG-binding domain protein 2 (MBD2)-based DNA enrichment for use on a semi-automated exclusion-based sample preparation (ESP) platform for robust and scalable enrichment of methylated DNA from low-input samples, called SEEMLIS. RESULTS: We show that combining methylation-sensitive enzyme digestion with ESP-based MBD2 enrichment allows for single gene analysis with high sensitivity for GSTP1 in highly impure, heterogenous samples. We also show that ESP-based MBD2 enrichment coupled with targeted pre-amplification allows for analysis of multiple genes with sensitivities approaching the single cell level in pure samples for GSTP1 and RASSF1 and sensitivity down to 14 cells for these genes in highly impure samples. Finally, we demonstrate the potential clinical utility of SEEMLIS by successful detection of methylated gene signatures in circulating tumor cells (CTCs) from patients with prostate cancer with varying CTC number and sample purity. CONCLUSIONS: SEEMLIS is a robust assay for targeted DNA methylation analysis in low-input samples, with flexibility at multiple steps. We demonstrate the feasibility of this assay to analyze DNA methylation in prostate cancer cells using CTCs from patients with prostate cancer as a real-world example of a low-input analyte of clinical importance. In summary, this novel assay provides a platform for determining methylation signatures in rare cell populations with broad implications for research as well as clinical applications.