Circulating exosomes contain a wealth of proteomic and genetic information, presenting an enormous opportunity in cancer diagnostics. While microfluidic approaches have been used to successfully ...isolate cells from complex samples, scaling these approaches for exosome isolation has been limited by the low throughput and susceptibility to clogging of nanofluidics. Moreover, the analysis of exosomal biomarkers is confounded by substantial heterogeneity between patients and within a tumor itself. To address these challenges, we developed a multichannel nanofluidic system to analyze crude clinical samples. Using this platform, we isolated exosomes from healthy and diseased murine and clinical cohorts, profiled the RNA cargo inside of these exosomes, and applied a machine learning algorithm to generate predictive panels that could identify samples derived from heterogeneous cancer-bearing individuals. Using this approach, we classified cancer and precancer mice from healthy controls, as well as pancreatic cancer patients from healthy controls, in blinded studies.
The liver is the most common site of metastatic disease
. 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 liver
. 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.
To determine whether a multianalyte liquid biopsy can improve the detection and staging of pancreatic ductal adenocarcinoma (PDAC).
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
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.
To identify patients with PDAC
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 (
= 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%;
< 0.05).
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.
Understanding the influence of gene expression on the molecular mechanisms underpinning human phenotypic diversity is fundamental to being able to predict health outcomes and treat disease. We have ...carried out whole transcriptome expression analysis on a series of eight normal human postmortem eyes by RNA sequencing. Here we present data showing that ∼80% of the transcriptome is expressed in the posterior layers of the eye and that there is significant differential expression not only between the layers of the posterior part of the eye but also between locations of a tissue layer. These differences in expression also extend to alternative splicing and splicing factors. Differentially expressed genes are enriched for genes associated with psychiatric, immune and cardiovascular disorders. Enrichment categories for gene ontology included ion transport, synaptic transmission and visual and sensory perception. Lastly, allele-specific expression was found to be significant for CFH, C3 and CFB, which are known risk genes for age-related macular degeneration. These expression differences should be useful in determining the underlying biology of associations with common diseases of the human retina, retinal pigment epithelium and choroid and in guiding the analysis of the genomic regions involved in the control of normal gene expression.
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.
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).
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,
= 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);
< 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);
= 0.061. Mutations in
exon 20,
, or
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);
< 0.001 and OS HR, 0.31 (0.13-0.74);
= 0.009.
pTMB ≥ 16 mut/Mb is associated with improved PFS after first-line standard-of-care pembrolizumab-based therapy in mNSCLC.
and
mutations may help identify pTMB-high patients unlikely to respond. These results should be validated in larger prospective studies.
The clinical utility of plasma cell-free DNA (cfDNA) has not been assessed prospectively in patients with glioblastoma (GBM). We aimed to determine the prognostic impact of plasma cfDNA in GBM, as ...well as its role as a surrogate of tumor burden and substrate for next-generation sequencing (NGS).
We conducted a prospective cohort study of 42 patients with newly diagnosed GBM. Plasma cfDNA was quantified at baseline prior to initial tumor resection and longitudinally during chemoradiotherapy. Plasma cfDNA was assessed for its association with progression-free survival (PFS) and overall survival (OS), correlated with radiographic tumor burden, and subjected to a targeted NGS panel.
Prior to initial surgery, GBM patients had higher plasma cfDNA concentration than age-matched healthy controls (mean 13.4 vs. 6.7 ng/mL,
< 0.001). Plasma cfDNA concentration was correlated with radiographic tumor burden on patients' first post-radiation magnetic resonance imaging scan (ρ = 0.77,
= 0.003) and tended to rise prior to or concurrently with radiographic tumor progression. Preoperative plasma cfDNA concentration above the mean (>13.4 ng/mL) was associated with inferior PFS (median 4.9 vs. 9.5 months,
= 0.038). Detection of ≥1 somatic mutation in plasma cfDNA occurred in 55% of patients and was associated with nonstatistically significant decreases in PFS (median 6.0 vs. 8.7 months,
= 0.093) and OS (median 5.5 vs. 9.2 months,
= 0.053).
Plasma cfDNA may be an effective prognostic tool and surrogate of tumor burden in newly diagnosed GBM. Detection of somatic alterations in plasma is feasible when samples are obtained prior to initial surgical resection.
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.
To determine whether plasma NGS testing was associated with improved mutation detection and enhanced delivery of personalized therapy in a real-world clinical setting.
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).
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.
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).
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.
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.
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