Hepatocytes direct the formation of a pro-metastatic niche in the liver.

The liver is the most common site of metastatic disease1. Although this metastatic tropism may reflect the mechanical trapping of circulating tumour cells, liver metastasis is also dependent, at least in part, on the formation of a 'pro-metastatic' niche that supports the spread of tumour cells to the liver2,3. The mechanisms that direct the formation of this niche are poorly understood. Here we show that hepatocytes coordinate myeloid cell accumulation and fibrosis within the liver and, in doing so, increase the susceptibility of the liver to metastatic seeding and outgrowth. During early pancreatic tumorigenesis in mice, hepatocytes show activation of signal transducer and activator of transcription 3 (STAT3) signalling and increased production of serum amyloid A1 and A2 (referred to collectively as SAA). Overexpression of SAA by hepatocytes also occurs in patients with pancreatic and colorectal cancers that have metastasized to the liver, and many patients with locally advanced and metastatic disease show increases in circulating SAA. Activation of STAT3 in hepatocytes and the subsequent production of SAA depend on the release of interleukin 6 (IL-6) into the circulation by non-malignant cells. Genetic ablation or blockade of components of IL-6-STAT3-SAA signalling prevents the establishment of a pro-metastatic niche and inhibits liver metastasis. Our data identify an intercellular network underpinned by hepatocytes that forms the basis of a pro-metastatic niche in the liver, and identify new therapeutic targets.

Multianalyte Prognostic Signature Including Circulating Tumor DNA and Circulating Tumor Cells in Patients With Advanced Pancreatic Adenocarcinoma.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is associated with a poor prognosis. Multianalyte signatures, including liquid biopsy and traditional clinical variables, have shown promise for improving prognostication in other solid tumors but have not yet been rigorously assessed for PDAC. MATERIALS AND METHODS: We performed a prospective cohort study of patients with newly diagnosed locally advanced pancreatic cancer (LAPC) or metastatic PDAC (mPDAC) who were planned to undergo systemic therapy. We collected peripheral blood before systemic therapy and assessed circulating tumor cells (CTCs), cell-free DNA concentration (cfDNA), and circulating tumor KRAS (ctKRAS)-variant allele fraction (VAF). Association of variables with overall survival (OS) was assessed in univariate and multivariate survival analysis, and comparisons were made between models containing liquid biopsy variables combined with traditional clinical prognostic variables versus models containing traditional clinical prognostic variables alone. RESULTS: One hundred four patients, 40 with LAPC and 64 with mPDAC, were enrolled. CTCs, cfDNA concentration, and ctKRAS VAF were all significantly higher in patients with mPDAC than patients with LAPC. ctKRAS VAF (cube root; 0.05 unit increments; hazard ratio, 1.11; 95% CI, 1.03 to 1.21; P = .01), and CTCs ≥ 1/mL (hazard ratio, 2.22; 95% CI, 1.34 to 3.69; P = .002) were significantly associated with worse OS in multivariate analysis while cfDNA concentration was not. A model selected by backward selection containing traditional clinical variables plus liquid biopsy variables had better discrimination of OS compared with a model containing traditional clinical variables alone (optimism-corrected Harrell's C-statistic 0.725 v 0.681). CONCLUSION: A multianalyte prognostic signature containing CTCs, ctKRAS, and cfDNA concentration outperformed a model containing traditional clinical variables alone suggesting that CTCs, ctKRAS, and cfDNA provide prognostic information complementary to traditional clinical variables in advanced PDAC.

Association of plasma cell-free DNA with survival in patients with IDH wild-type glioblastoma.

BACKGROUND: We aimed to determine whether plasma cell-free DNA (cfDNA) concentration is associated with survival in patients with isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM). METHODS: Pre-operative and post-chemoradiotherapy blood samples were prospectively collected from patients with newly diagnosed IDH wild-type GBM. Patients underwent surgical resection or biopsy and received adjuvant radiotherapy with concomitant temozolomide. Cell-free DNA (cfDNA) was isolated from plasma and quantified using SYBR Green-based q polymerase chain reaction (qPCR). RESULTS: Sixty-two patients were enrolled and categorized into high vs. low cfDNA groups relative to the pre-operative median value (25.2 ng/mL, range 5.7-153.0 ng/mL). High pre-operative cfDNA concentration was associated with inferior PFS (median progression-free survival (PFS), 3.4 vs. 7.7 months; log-rank P = .004; hazard ratio [HR], 2.19; 95% CI, 1.26-3.81) and overall survival (OS) (median OS, 8.0 vs. 13.9 months; log-rank P = .01; HR, 2.43; 95% CI, 1.19-4.95). After adjusting for risk factors, including O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, pre-operative cfDNA remained independently associated with PFS (HR, 2.70; 95% CI, 1.50-4.83; P = .001) and OS (HR, 2.65; 95% CI, 1.25-5.59; P = .01). Post-hoc analysis of change in cfDNA post-chemoradiotherapy compared to pre-surgery (n = 24) showed increasing cfDNA concentration was associated with worse PFS (median, 2.7 vs. 6.0 months; log-rank P = .003; HR, 4.92; 95% CI, 1.53-15.84) and OS (median, 3.9 vs. 19.4 months; log-rank P < .001; HR, 7.77; 95% CI, 2.17-27.76). CONCLUSIONS: cfDNA concentration is a promising prognostic biomarker for patients with IDH wild-type GBM. Plasma cfDNA can be obtained noninvasively and may enable more accurate estimates of survival and effective clinical trial stratification.

Optimization of Sources of Circulating Cell-Free DNA Variability for Downstream Molecular Analysis.

Circulating cell-free DNA (ccfDNA) is used increasingly as a cancer biomarker for prognostication, as a correlate for tumor volume, or as input for downstream molecular analysis. Determining optimal blood processing and ccfDNA quantification are crucial for ccfDNA to serve as an accurate biomarker as it moves into the clinical realm. Whole blood was collected from 50 subjects, processed to plasma, and used immediately or frozen at -80°C. Plasma ccfDNA was extracted and concentration was assessed by real-time quantitative PCR (qPCR), fluorimetry, and droplet digital PCR (ddPCR). For the 24 plasma samples from metastatic pancreatic cancer patients, the variant allele fractions (VAF) of KRAS G12/13 pathogenic variants in circulating tumor DNA (ctDNA) were measured by ddPCR. Using a high-speed (16,000 × g) or slower-speed (4100 × g) second centrifugation step showed no difference in ccfDNA yield or ctDNA VAF. A two- versus three-spin centrifugation protocol also showed no difference in ccfDNA yield or ctDNA VAF. A higher yield was observed from fresh versus frozen plasma by qPCR and fluorimetry, whereas a higher yield was observed for frozen versus fresh plasma by ddPCR, however, no difference was observed in ctDNA VAF. Overall, our findings suggest factors to consider when implementing a ccfDNA extraction and quantification workflow in a research or clinical setting.

Combining radiomic phenotypes of non-small cell lung cancer with liquid biopsy data may improve prediction of response to EGFR inhibitors.

Among non-small cell lung cancer (NSCLC) patients with therapeutically targetable tumor mutations in epidermal growth factor receptor (EGFR), not all patients respond to targeted therapy. Combining circulating-tumor DNA (ctDNA), clinical variables, and radiomic phenotypes may improve prediction of EGFR-targeted therapy outcomes for NSCLC. This single-center retrospective study included 40 EGFR-mutant advanced NSCLC patients treated with EGFR-targeted therapy. ctDNA data included number of mutations and detection of EGFR T790M. Clinical data included age, smoking status, and ECOG performance status. Baseline chest CT scans were analyzed to extract 429 radiomic features from each primary tumor. Unsupervised hierarchical clustering was used to group tumors into phenotypes. Kaplan-Meier (K-M) curves and Cox proportional hazards regression were modeled for progression-free survival (PFS) and overall survival (OS). Likelihood ratio test (LRT) was used to compare fit between models. Among 40 patients (73% women, median age 62 years), consensus clustering identified two radiomic phenotypes. For PFS, the model combining radiomic phenotypes with ctDNA and clinical variables had c-statistic of 0.77 and a better fit (LRT p = 0.01) than the model with clinical and ctDNA variables alone with a c-statistic of 0.73. For OS, adding radiomic phenotypes resulted in c-statistic of 0.83 versus 0.80 when using clinical and ctDNA variables (LRT p = 0.08). Both models showed separation of K-M curves dichotomized by median prognostic score (p < 0.005). Combining radiomic phenotypes, ctDNA, and clinical variables may enhance precision oncology approaches to managing advanced non-small cell lung cancer with EGFR mutations.

Serial Monitoring of Circulating Tumor DNA by Next-Generation Gene Sequencing as a Biomarker of Response and Survival in Patients With Advanced NSCLC Receiving Pembrolizumab-Based Therapy.

Although the majority of patients with metastatic non-small-cell lung cancer (mNSCLC) lacking a detectable targetable mutation will receive pembrolizumab-based therapy in the frontline setting, predicting which patients will experience a durable clinical benefit (DCB) remains challenging. MATERIALS AND METHODS: Patients with mNSCLC receiving pembrolizumab monotherapy or in combination with chemotherapy underwent a 74-gene next-generation sequencing panel on blood samples obtained at baseline and at 9 weeks. The change in circulating tumor DNA levels on-therapy (molecular response) was quantified using a ratio calculation with response defined by a > 50% decrease in mean variant allele fraction. Patient response was assessed using RECIST 1.1; DCB was defined as complete or partial response or stable disease that lasted > 6 months. Progression-free survival and overall survival were recorded. RESULTS: Among 67 patients, 51 (76.1%) had > 1 variant detected at a variant allele fraction > 0.3% and thus were eligible for calculation of molecular response from paired baseline and 9-week samples. Molecular response values were significantly lower in patients with an objective radiologic response (log mean 1.25% v 27.7%, P < .001). Patients achieving a DCB had significantly lower molecular response values compared to patients with no durable benefit (log mean 3.5% v 49.4%, P < .001). Molecular responders had significantly longer progression-free survival (hazard ratio, 0.25; 95% CI, 0.13 to 0.50) and overall survival (hazard ratio, 0.27; 95% CI, 0.12 to 0.64) compared with molecular nonresponders. CONCLUSION: Molecular response assessment using circulating tumor DNA may serve as a noninvasive, on-therapy predictor of response to pembrolizumab-based therapy in addition to standard of care imaging in mNSCLC. This strategy requires validation in independent prospective studies.

Blood-based gene expression signature associated with metastatic castrate-resistant prostate cancer patient response to abiraterone plus prednisone or enzalutamide.

BACKGROUND: Precision medicine approaches for managing patients with metastatic castrate-resistant prostate cancer (mCRPC) are lacking. Non-invasive approaches for molecular monitoring of disease are urgently needed, especially for patients suffering from bone metastases for whom tissue biopsy is challenging. Here we utilized baseline blood samples to identify mCRPC patients most likely to benefit from abiraterone plus prednisone (AAP) or enzalutamide. METHODS: Baseline blood samples were collected for circulating tumor cell (CTC) enumeration and qPCR-based gene expression analysis from 51 men with mCRPC beginning treatment with abiraterone or enzalutamide. RESULTS: Of 51 patients (median age 68 years [51-82]), 22 received AAP (abiraterone 1000 mg/day plus prednisone 10 mg/day) and 29 received enzalutamide (160 mg/day). The cohort was randomly divided into training (n = 37) and test (n = 14) sets. Baseline clinical variables (Gleason score, PSA, testosterone, and hemoglobin), CTC count, and qPCR-based gene expression data for 141 genes/isoforms in CTC-enriched blood were analyzed with respect to overall survival (OS). Genes with expression most associated with OS included MSLN, ARG2, FGF8, KLK3, ESRP2, NPR3, CCND1, and WNT5A. Using a Cox-elastic net model for our test set, the 8-gene expression signature had a c-index of 0.87 (95% CI [0.80, 0.94]) and was more strongly associated with OS than clinical variables or CTC count alone, or a combination of the three variables. For patients with a low-risk vs. high-risk gene expression signature, median OS was not reached vs. 18 months, respectively (HR 5.32 [1.91-14.80], p = 0.001). For the subset of 41 patients for whom progression-free survival (PFS) data was available, the median PFS for patients with a low-risk vs high-risk gene expression signature was 20 vs. 5 months, respectively (HR 2.95 [1.46-5.98], p = 0.003). CONCLUSIONS: If validated in a larger prospective study, this test may predict patients most likely to benefit from second-generation antiandrogen therapy.

A Multianalyte Panel Consisting of Extracellular Vesicle miRNAs and mRNAs, cfDNA, and CA19-9 Shows Utility for Diagnosis and Staging of Pancreatic Ductal Adenocarcinoma.

PURPOSE: To determine whether a multianalyte liquid biopsy can improve the detection and staging of pancreatic ductal adenocarcinoma (PDAC). EXPERIMENTAL DESIGN: We analyzed plasma from 204 subjects (71 healthy, 44 non-PDAC pancreatic disease, and 89 PDAC) for the following biomarkers: tumor-associated extracellular vesicle miRNA and mRNA isolated on a nanomagnetic platform that we developed and measured by next-generation sequencing or qPCR, circulating cell-free DNA (ccfDNA) concentration measured by qPCR, ccfDNA KRAS G12D/V/R mutations detected by droplet digital PCR, and CA19-9 measured by electrochemiluminescence immunoassay. We applied machine learning to training sets and subsequently evaluated model performance in independent, user-blinded test sets. RESULTS: To identify patients with PDAC versus those without, we generated a classification model using a training set of 47 subjects (20 PDAC and 27 noncancer). When applied to a blinded test set (N = 136), the model achieved an AUC of 0.95 and accuracy of 92%, superior to the best individual biomarker, CA19-9 (89%). We next used a cohort of 20 patients with PDAC to train our model for disease staging and applied it to a blinded test set of 25 patients clinically staged by imaging as metastasis-free, including 9 subsequently determined to have had occult metastasis. Our workflow achieved significantly higher accuracy for disease staging (84%) than imaging alone (accuracy = 64%; P < 0.05). CONCLUSIONS: Algorithmically combining blood-based biomarkers may improve PDAC diagnostic accuracy and preoperative identification of nonmetastatic patients best suited for surgery, although larger validation studies are necessary.

Baseline Plasma Tumor Mutation Burden Predicts Response to Pembrolizumab-based Therapy in Patients with Metastatic Non-Small Cell Lung Cancer.

PURPOSE: The role of plasma-based tumor mutation burden (pTMB) in predicting response to pembrolizumab-based first-line standard-of-care therapy for metastatic non-small cell lung cancer (mNSCLC) has not been explored. EXPERIMENTAL DESIGN: A 500-gene next-generation sequencing panel was used to assess pTMB. Sixty-six patients with newly diagnosed mNSCLC starting first-line pembrolizumab-based therapy, either alone or in combination with chemotherapy, were enrolled (Clinicaltrial.gov identifier: NCT03047616). Response was assessed using RECIST 1.1. Associations were made for patient characteristics, 6-month durable clinical benefit (DCB), progression-free survival (PFS), and overall survival (OS). RESULTS: Of 66 patients, 52 (78.8%) were pTMB-evaluable. Median pTMB was 16.8 mutations per megabase (mut/Mb; range, 1.9-52.5) and was significantly higher for patients achieving DCB compared with no durable benefit (21.3 mut/Mb vs. 12.4 mut/Mb, P = 0.003). For patients with pTMB ≥ 16 mut/Mb, median PFS was 14.1 versus 4.7 months for patients with pTMB < 16 mut/Mb [HR, 0.30 (0.16-0.60); P < 0.001]. Median OS for patients with pTMB ≥ 16 was not reached versus 8.8 months for patients with pTMB < 16 mut/Mb [HR, 0.48 (0.22-1.03); P = 0.061]. Mutations in ERBB2 exon 20, STK11, KEAP1, or PTEN were more common in patients with no DCB. A combination of pTMB ≥ 16 and absence of negative predictor mutations was associated with PFS [HR, 0.24 (0.11-0.49); P < 0.001] and OS [HR, 0.31 (0.13-0.74); P = 0.009]. CONCLUSIONS: pTMB ≥ 16 mut/Mb is associated with improved PFS after first-line standard-of-care pembrolizumab-based therapy in mNSCLC. STK11/KEAP1/PTEN and ERBB2 mutations may help identify pTMB-high patients unlikely to respond. These results should be validated in larger prospective studies.

Correction: Flow based single cell analysis of the immune landscape distinguishes Barrett's esophagus from adjacent normal tissue.

[This corrects the article DOI: 10.18632/oncotarget.26911.].

Flow based single cell analysis of the immune landscape distinguishes Barrett's esophagus from adjacent normal tissue.

Barrett's esophagus (BE) is metaplasia of the squamous epithelium to a specialized columnar epithelium. BE progresses through low- and high-grade dysplasia before developing into esophageal adenocarcinoma. The BE microenvironment is not well defined. We compare 12 human clinical BE and adjacent normal squamous epithelium biopsies using single cell immunophenotyping by flow cytometry. A cassette of 19 epithelial and immune cell markers was used to detect differences between cellular compartments in normal and BE tissues. We found that the BE microenvironment has an immunological landscape distinct from adjacent normal epithelium. BE has an increased percentage of epithelial cells with a concomitant decrease in the percentage of immune cells, accompanied by a shift in the immune landscape from a predominantly T cell rich microenvironment in normal tissue to a B cell rich landscape in BE tissue. Hierarchical clustering separates BE and normal samples into two discrete groups based upon our 19-marker panel, but also reveals unexpected, shared phenotypes for three patients. Our results suggest that flow based single cell analysis may have the potential for revealing clinically relevant differences between BE and normal adjacent tissue, and that surface immunophenotypes could identify specific subpopulations from dysplastic tissue for further investigation.

Clinical Implications of Plasma-Based Genotyping With the Delivery of Personalized Therapy in Metastatic Non-Small Cell Lung Cancer.

Importance: The clinical implications of adding plasma-based circulating tumor DNA next-generation sequencing (NGS) to tissue NGS for targetable mutation detection in non-small cell lung cancer (NSCLC) have not been formally assessed. Objective: To determine whether plasma NGS testing was associated with improved mutation detection and enhanced delivery of personalized therapy in a real-world clinical setting. Design, Setting, and Participants: This prospective cohort study enrolled 323 patients with metastatic NSCLC who had plasma testing ordered as part of routine clinical management. Plasma NGS was performed using a 73-gene commercial platform. Patients were enrolled at the Hospital of the University of Pennsylvania from April 1, 2016, through January 2, 2018. The database was locked for follow-up and analyses on January 2, 2018, with a median follow-up of 7 months (range, 1-21 months). Main Outcomes and Measures: The number of patients with targetable alterations detected with plasma and tissue NGS; the association between the allele fractions (AFs) of mutations detected in tissue and plasma; and the association of response rate with the plasma AF of the targeted mutations. Results: Among the 323 patients with NSCLC (60.1% female; median age, 65 years [range, 33-93 years]), therapeutically targetable mutations were detected in EGFR, ALK, MET, BRCA1, ROS1, RET, ERBB2, or BRAF for 113 (35.0%) overall. Ninety-four patients (29.1%) had plasma testing only at the discretion of the treating physician or patient preference. Among the 94 patients with plasma testing alone, 31 (33.0%) had a therapeutically targetable mutation detected, thus obviating the need for an invasive biopsy. Among the remaining 229 patients who had concurrent plasma and tissue NGS or were unable to have tissue NGS, a therapeutically targetable mutation was detected in tissue alone for 47 patients (20.5%), whereas the addition of plasma testing increased this number to 82 (35.8%). Thirty-six of 42 patients (85.7%) who received a targeted therapy based on the plasma result achieved a complete or a partial response or stable disease. The plasma-based targeted mutation AF had no correlation with depth of Response Evaluation Criteria in Solid Tumors response (r = -0.121; P = .45). Conclusions and Relevance: Integration of plasma NGS testing into the routine management of stage IV NSCLC demonstrates a marked increase of the detection of therapeutically targetable mutations and improved delivery of molecularly guided therapy.

Feasibility of monitoring advanced melanoma patients using cell-free DNA from plasma.

To determine the feasibility of liquid biopsy for monitoring of patients with advanced melanoma, cell-free DNA was extracted from plasma for 25 Stage III/IV patients, most (84.0%) having received previous therapy. DNA concentrations ranged from 0.6 to 390.0 ng/ml (median = 7.8 ng/ml) and were positively correlated with tumor burden as measured by imaging (Spearman rho = 0.5435, p = .0363). Using ultra-deep sequencing for a 61-gene panel, one or more mutations were detected in 12 of 25 samples (48.0%), and this proportion did not vary significantly for patients on or off therapy at the time of blood draw (52.9% and 37.5% respectively; p = .673). Sixteen mutations were detected in eight different genes, with the most frequent mutations detected in BRAF, NRAS, and KIT. Allele fractions ranged from 1.1% to 63.2% (median = 29.1%). Among patients with tissue next-generation sequencing, nine of 11 plasma mutations were also detected in matched tissue, for a concordance of 81.8%.