Patent Application
20210292844
The material in the accompanying sequence listing is hereby incorporated by reference into this application. The accompanying sequence listing text file, named USC1300-1_ST25.txt, was created on Jun. 6, 2019, and is 551 KB. The file can be accessed using Microsoft Word on a computer that uses Windows OS.
The present invention relates to methods of screening and diagnosing prostate cancer.
Prostate cancer (PC) is the most frequently diagnosed cancer and the second most common cause of cancer deaths in men in the United States. Although PC incidence rates have increased over the past 25 years, mortality rates have largely remained unchanged (www.cancer.gov). The advent of a PC diagnostic test, which measures the level of prostate specific antigen (PSA), has resulted in the discovery of earlier, smaller lesions, as well as increased the need for prognostic tools to avoid overtreatment of indolent PC1. Localized PC is curable through radical prostatectomy or radiation therapy, albeit often followed by significant side effects, including urinary incontinence (5-20%), erectile dysfunction (30-70%), and bowel toxicity (5-10%)2,3. Generally, PC is a slow-growing malignancy with decades of indolence, but takes on an aggressive form displaying rapid growth, dissemination, and lethality in a subset of cases (<20%)1,4.
Predicting tumor progression is critical for clinical decision-making. PC is graded using the Gleason system, in which tumors with higher Gleason Scores (GSs) tend to be more aggressive5,6. However, the relationship between individual GS of clinically-localized PCs and those that progress to metastatic disease is poorly understood7. The tumorigenic events during PC progression have been difficult to investigate, and the ability to characterize late stages of PC progression is lacking due to very limited availability of metastatic tissues. In addition, 60-90% of PCs are multifocal8, in which one prostate contains several seemingly unconnected locations of cancer growth. The development of multifocal PC is still highly debated, and two models have been described. Either one initially-transformed cancer has spread to multiple locations within the prostate (monoclonal), and/or the cancer arises independently several times in the same gland (multiple subclones)9-18. The latter option indicates the possibility that aggressive and non-aggressive cancer foci co-exist in the same prostate gland, and is supported by the finding that individual foci of multifocal PC often present with unique GSs19. Consequently, the index lesion (the cancer lesion with the largest volume or the highest GS depending on the study) may not be representative of the PC pathology20, and subsequently complicates sample selection for analysis. Therefore, previous studies that have not accounted for prostate tumor multifocality, or used only the index lesion, are potentially flawed.
Currently, no curative therapies are available for metastatic PC. Thus, a central dilemma in management of clinically localized PC is whether to postpone treatment until the disease becomes more aggressive in order to minimize patient health side effects, or to treat immediately to avoid dissemination. Identifying the primary lesion that drives cancer progression is critically advantageous in order to guide treatment decisions at the time of diagnosis. In this scenario, ideally, only patients with potential aggressive PC lesion(s) should undergo surgery, while patients with exclusively clinically insignificant (non-aggressive) cancer should be monitored using active surveillance.
DNA methylation alterations occur in every cancer type, and importantly, DNA methylation levels change concordantly with tumor aggressiveness in most types of cancer21. Epigenetic alterations can drive tumorigenesis and determine tumor aggressiveness, and therefore, can be used for PC diagnostic purposes22, as well as to inform on therapeutic approaches23,53. Although PC has been shown to harbor a great hereditary element24,25, only an estimated ˜30% of these factors have presently been accounted for26. Interestingly, recent studies have been able to tie a connection between genetic alterations and DNA methylation changes, which indicate that DNA methylation changes hold information about clonal evolution of PC. For example, multiple metastases within a PC patient have been shown to arise from a single precursor cancer cell, or focus, by copy number alterations (CNA) as well as by DNA methylation changes27,28,54, suggesting that only one focus of a multifocal PC is responsible for the development of the metastatic lesions. Moreover, unified evolution of DNA methylation and CNAs was identified in five cases of multifocal PC with monoclonal origin and their matched lymph node metastases11.
One aspect of the present invention is directed to a method of identifying subjects having prostate cancer. The method includes obtaining a sample from a subject, isolating DNA from the sample, determining the methylation status of the DNA and comparing the methylation status of the DNA to one or more methylated probes selected from the following: cg00697992, cg01272707, cg01819167, cg01906055, cg02160684, cg02560085, cg03195164, cg03456213, cg04634417, cg06024295, cg11748187, cg13300630, cg13944838, cg14399930, cg15132013, cg16469740, cg17004353, cg17032646, cg18315943, cg19550524, cg20399616, cg24517686, cg25961816, cg26447413 and cg27198013. The methylated probe includes a sequence region that extends up to 250 base pairs upstream and downstream from the methylated probe. The comparison indicates whether the sample is normal, a non-aggressive prostate cancer, or an aggressive prostate cancer.
Examples of methylation sensitive assays that can be used to determine the DNA methylation status include but are not limited to HM450, HM850, real-time methylation sensitive PCR (MSP), MethyLight and Pyrosequencing.
In one embodiment, the sample is a biopsy sample.
In another embodiment, the biopsy is from a prostate.
In another embodiment, the biopsy is from a pelvic lymph node.
In another embodiment, two or more methylated probes are selected.
In another embodiment, the sample is selected from the following: blood, plasma and urine.
In another embodiment, the sequence region extends up to 100 base pairs upstream and downstream from the methylated probe.
In another embodiment, the sequence region extends 0 base pairs upstream and downstream from the methylated probe.
In another embodiment, five or more methylated probes are selected.
In another embodiment, fifteen or more methylated probes are selected.
Another aspect of the present invention is to provide a method of classifying prostate cancer. The method includes obtaining a sample from a subject, isolating DNA from the sample, determining the methylation status of the DNA, and comparing the methylation status of the DNA to one or more methylated probes selected from the following: cg00697992, cg01272707, cg01819167, cg01906055, cg02160684, cg02560085, cg03195164, cg03456213, cg04634417, cg06024295, cg11748187, cg13300630, cg13944838, cg14399930, cg15132013, cg16469740, cg17004353, cg17032646, cg18315943, cg19550524, cg20399616, cg24517686, cg25961816, cg26447413 and cg27198013. The methylated probe includes a sequence region that extends up to 250 base pairs upstream and downstream from the methylated probe. The comparison indicates whether the prostate cancer is non-aggressive or aggressive.
In one embodiment, the sample is a biopsy sample.
In another embodiment, the biopsy is from a prostate.
In another embodiment, the biopsy is from a pelvic lymph node.
In another embodiment, two or more methylated probes are selected.
In another embodiment, the sample is selected from the following: blood, plasma and urine.
In another embodiment, the sequence region extends up to 100 base pairs upstream and downstream from the methylated probe.
In another embodiment, the sequence region extends 0 base pairs upstream and downstream from the methylated probe.
In another embodiment, five or more methylated probes are selected.
In another embodiment, fifteen or more methylated probes are selected.
Another aspect of the present invention is directed to a method of identifying subjects having prostate cancer. The method includes obtaining a sample from a subject, isolating DNA from the sample, determining the methylation status of the DNA and comparing the methylation status of the DNA to one or more methylated probes selected from the following: cg20399616, cg12737160, cg23624008, cg09789590, cg23095615, cg14273822, cg04294888, cg12098872, cg21981270, cg21526205, cg09806262, cg01940855, cg23855505, cg18423852, cg22260952, cg21883802, cg00036011, cg26573704, cg12727795 and cg01404317. The methylated probe includes a sequence region that extends up to 250 base pairs upstream and downstream from the methylated probe. The comparison indicates whether the sample is normal or prostate cancer.
In one embodiment, the sample is a biopsy sample.
In another embodiment, the biopsy is from a prostate.
In another embodiment, the biopsy is from a pelvic lymph node.
In another embodiment, two or more methylated probes are selected.
In another embodiment, the sample is selected from the following: blood, plasma and urine.
In another embodiment, the sequence region extends up to 100 base pairs upstream and downstream from the methylated probe.
In another embodiment, the sequence region extends 0 base pairs upstream and downstream from the methylated probe.
In another embodiment, five or more methylated probes are selected.
In another embodiment, fifteen or more methylated probes are selected.
Another aspect of the present invention is to provide a method of classifying prostate cancer. The method includes obtaining a sample from a subject, isolating DNA from the sample, determining the methylation status of the DNA, and comparing the methylation status of the DNA to one or more methylated probes selected from the following: cg25729795, cg09771468, cg01386424, cg21397588, cg18595258, cg01957088, cg12950441, cg05733231, cg19155518, cg13060157, cg16201038, cg01906055, cg16473141, cg17380661, cg04145287, cg22462983, cg01419991, cg06969287, cg04634417 and cg16692973. The methylated probe includes a sequence region that extends up to 250 base pairs upstream and downstream from the methylated probe. The comparison indicates whether the prostate cancer is non-aggressive or aggressive.
In one embodiment, the sample is a biopsy sample.
In another embodiment, the biopsy is from a prostate.
In another embodiment, the biopsy is from a pelvic lymph node.
In another embodiment, two or more methylated probes are selected.
In another embodiment, the sample is selected from the following: blood, plasma and urine.
In another embodiment, the sequence region extends up to 100 base pairs upstream and downstream from the methylated probe.
In another embodiment, the sequence region extends 0 base pairs upstream and downstream from the methylated probe.
In another embodiment, five or more methylated probes are selected.
In another embodiment, fifteen or more methylated probes are selected.
In other aspects of the present invention, the methylated probes that are used in identifying subjects having prostate cancer and/or classifying prostate cancer can be one or more methylated probes selected from those methylated probes listed in Table 4. The sequences of all the probes listed in Table 4 are also available from Illumina.
Prostate Cancer (PC) is a slow-growing malignancy, but acquires aggressive traits in a subset of cases. PCs have a propensity to be multifocal. Here, we show that DNA methylation of PC metastases is highly similar to specific primary foci, build an aggressiveness classifier, and determined PC aggressiveness of foci based on DNA methylation profiles in matched primary tumors and metastases. The classifier is significant associated with aggressiveness and the presence of lymph node metastases (P=9.2×10−5) and invasive tumor stages (P=7.7×10−7) in an independent cohort. Overall, this invention provides molecular-based support for determining PC aggressiveness with importance for clinical decision-making.
Other aspects and advantages of the invention will be apparent from the following description and the appended claims.
A “biomarker” as used herein refers to a molecular indicator that is associated with a particular pathological or physiological state. The “biomarker” as used herein is a molecular indicator for cancer, more specifically an indicator for prostate cancer.
As used herein the term “cancer” refers to or describes the physiological condition in mammals that is typically characterized by abnormal and uncontrolled cell division or cell growth. Examples of cancer include but are not limited to, carcinoma, lymphoma, blastoma, sarcoma, and leukemia.
As used herein, a “subject” is preferably a human, non-human primate, cow, horse, pig, sheep, goat, dog, cat, or rodent. In all embodiments, human subjects are preferred. The “subject” may be at risk of developing prostate cancer, may be suspected of having prostate cancer, or may have prostate cancer. In addition, a “subject” may simply be a person who wants to be screened for prostate cancer.
In this study, aggressive primary cancer focus/foci can be identified from multifocal PC by the degree of correlation of DNA methylation to lymph node metastases, which represent an aggressive trait (
We used the Illumina HumanMethylation450 BeadArray (HM450) platform to measure the DNA methylation status of matched primary tumors and pelvic lymph node metastases in 16 patients with multifocal disease (Table 1). The prostate and lymph node tissue samples stored in FFPE tissue blocks were sectioned, H&E stained (
Sample purity was tested with respect to either infiltration of normal cells or leukocytes caused by inflammation (Methods,
In order to investigate if DNA methylation patterns hold information about clonal evolution in PC, Pearson correlations amongst all the samples were calculated, plotted, and visualized using heatmaps (
We first investigated the DNA methylation profiles of PC foci among individual patients. To identify the focus of origin of lymph node metastasis, we selected the top 1% most variably methylated probes between all samples for each patient, excluding PLs. The DNA methylation levels of these probes from all samples, including PLs, were then compared by unsupervised hierarchical clustering and heatmap visualization. Based on similar DNA methylation levels, PLs clustered with other samples, thereby providing information about the potential clonal relationship. Heatmaps after unsupervised clustering of these probes for two representative patients, Patients 41 and 54 (
The PL DNA methylation profile for Patient 41 was very similar to and clustered very closely with the T2 and T3 foci, while the T4 and T1 foci were more dissimilar, as shown by the dendrogram at the top of the heatmap. For this patient, the T2 and/or T3 foci are the most likely origin(s) of the metastasis. Furthermore, the physical juxtaposition of T2 and T3 in the prostate specimen (
In order to validate these findings, we took advantage of the recent evidence that the HM450 DNA methylation platform can also be used to determine CNAs by summing the methylated and unmethylated signal intensities of the probes30,31. This analysis provided additional evidence that the T2 and T3 foci were very similar to the PL in Patient 41. All had deletions on chromosomes 2, 10, 11, and 16 and gains on chromosomes 7, 8, and 10, however, these regions were not altered in the T1 or T4 foci, which show different CNA patterns (
Similarly, all PLs clustered with one or more primary tumor foci from the remaining 12 cases using our DNA methylation-based approach (
We next devised a DNA methylation-based PC aggressiveness classifier to categorize primary tumor foci as either aggressive or non-aggressive. The unsupervised hierarchical clustering approach effectively identifies the primary origin of lymph node metastases. However, in order to categorize the aggressiveness of individual foci in a quantitative, unbiased, and objective manner, we calculated Euclidean distances between any two samples within a patient using all filtered HM450 probes. Euclidean distance, like Pearson correlation, compares sample similarities, but maintains data variability, and is also superior for analysis of differential expression34. We divided the scale of Euclidean distances into discrete categories (aggressive, undecided, and non-aggressive) for all primary tumor foci. Since the purpose of this categorization method is to assemble groups of genuinely aggressive and non-aggressive tumors for biomarker development, we included a gap of 10 Euclidean distance units (undecided category) to reduce the risk of misclassification. Sample categorization for each patient is shown using DNA methylation-based phylogenetic trees where samples are colored as a function of aggressiveness (
Taken together, our developed categorization approach found that eight patients (Patients 23, 24, 26, 41, 43, 56, 84, and 98) showed independent DNA methylation profiles indicative of multiple subclones. Five patients (Patients 14, 17, 54, 85, and 88) showed similar DNA methylation patterns indicating a monoclonal origin, and one patient (Patient 52) was not categorized as either (
We next searched for differentially methylated probes between the aggressive and non-aggressive groups (FDR adjusted p<0.05), but found that the DNA methylation levels of no single probe were significantly different between the two groups. Using an FDR cutoff of 0.3, 231 probes were identified. Still, we continued to search for a set or panel of probes able to distinguish these groups from a larger panel. First, we generated a list of the 3,000 most differentially methylated probes between the assembled aggressive and non-aggressive groups based on mean DNA methylation differences (
One of ordinary skill in the art will recognize that the methylated probe can also include sequence regions that extend upstream and/or downstream from the methylated probe. For example, the probe region could also include 250 bases (or greater) upstream and/or downstream from the probe, 200 bases upstream and/or downstream from the probe, 100 bases upstream and/or downstream from the probe, etc.
To test the classifier on an independent dataset, we took advantage of the publically available prostate adenocarcinoma HM450 DNA methylation data and accompanying clinical information from The Cancer Genome Atlas (TCGA) project. We tested 496 prostate samples (adjacent normal and tumor) using the classifier, and 70% of the samples (351 samples; 39 adjacent normal and 312 tumor samples) were predicted with a probability above 0.67 (
To evaluate the prognostic performance of the classifier, we consulted available clinicopathological co-variates known to be associated with PC aggressiveness (pre-operative PSA, tumor size, pathological GS, presence of lymph node metastases, and tumor stage) for samples with probabilities above 0.67. Aggressiveness was significantly (P<0.02) associated with the investigated co-variates except tumor size (
Upon further examination, tumors with high GSs (GS 8-10) were significantly associated with the aggressive group (P=0.022), but no such association was seen for tumors with low (GS 6) and intermediate (GS 7) scores (P=0.059 and P=0.254, respectively;
Importantly, we found that significantly more patients classified as having an aggressive PC presented with lymph node metastases at the time of surgery as compared to patients with predicted non-aggressive tumors (P=9.2×10−5;
Sixteen (16) patients diagnosed with multifocal PC having metastasized to one or more pelvic lymph nodes were enrolled in the study (Table 1). All patients had radical prostatectomies and removal of pelvic lymph nodes in the period between 1991-2013. No antiandrogen treatments were administered prior to surgery. The prostate and lymph node tissue samples were stored in FFPE tissue blocks. FFPE blocks were sectioned and H&E stained (
The dissected tissue samples were deparaffinized using a double xylene wash followed by a double ethanol wash and drying of the pellets. For DNA extraction, the pellets were resuspended in 240 μl of PKD buffer and Proteinase K (Qiagen, miRNeasy FFPE kit (cat no. 217504)), and then incubated at 55° C. overnight and 85° C. for 15 min. After cooling the samples, 500 μl RBC buffer was added and the samples were run through gDNA Eliminator columns (RNeasy plus mini kit (cat no. 74134)) using RPE buffer to wash and EB buffer for elution.
Genomic DNA (200-500 ng) from each FFPE sample was treated with sodium bisulfite and recovered using the Zymo EZ DNA methylation kit (Zymo Research, Irvine, Calif.) according to the manufacturer's specifications and eluted in 10 μl volume. An aliquot (1 μl) was removed for MethyLight-based quality control testing of bisulfite conversion completeness and the amount of bisulfite converted DNA available for the Infinium Methylation assay42. All samples that pass the QC tests were then repaired using the Illumina Restoration solution as described by the manufacturer. Each sample was then processed in the Infinium DNA methylation assay data production pipeline43.
After the chemistry steps, BeadArrays were scanned and the raw signal intensities were extracted from the *.IDAT files using the R package methylumi. The intensities were corrected for background fluorescence and red-green dye-bias44. The beta values were calculated as (M/(M+U)), in which M and U refer to the (pre-processed) mean methylated and unmethylated probe signal intensities, respectively. Measurements in which the fluorescent intensity was not statistically significantly above background signal (detection P-value>0.05) were removed from the data set. In addition, probes that overlap with known SNPs as well as repetitive elements were masked prior to data analyses. Specifically, all HM450 probes that overlapped with common SNPs with a minor allele frequency of greater than 1% (UCSC criteria) at the targeted CpG site, as well as probes with SNPs (MAF>1%) within 10 bp of the targeted CpG site were masked. HM450 probes that were within 15 bases of the CpG lying entirely within a repeat region were also masked prior to data analyses. The end result was a dataset of corrected beta-values for 396,020 probes which cover 21,000 genes.
To investigate the degree of leukocyte infiltration in each sample, public HM450 data from 96 male peripheral blood samples (GSE53740 and GSE51388) were downloaded using Marmal-aid45. All HM450 probes with beta values>0.2 in male peripheral blood were excluded. The remaining probes were used to subset 500 probes that were hypermethylated in 43 normal prostate samples from TCGA, and thus hypomethylated in blood. Tissues of prostate origin from our study with mean DNA methylation of these probes below 0.6 were excluded from further analysis. Two lymph node metastases were excluded due to high blood content. Four GSTP1 HM450 probes (cg06928838, cg09038676, cg22224704, cg26250609) were used for tumor purity analysis as described in Brocks et al.11. Primary tumors with mean DNA methylation below 0.4 were excluded from further analysis. Two tumor samples were excluded due to high normal content.
For each patient, probes with missing beta values (detection P-value>0.05) were excluded and the top 1% most variably methylated probes between all the samples except the PL(s) were selected. Heatmaps were used to display the DNA methylation levels and the unsupervised hierarchical clustering was performed with the hclust function in R (method=“complete”).
The CNAs were analyzed using the Champ package for R46 using 28 adjacent normal prostate samples purified from FFPE tissues (12 from this study and 16 from own unpublished data) as a reference. Imported beta values were run through champ.norm and champ.CNA (filterXY=FALSE, batchCorrect=T, freqThreshold=0.3). The generated segment mean-files were intersected with the Infinium probe locations using BedTools and the resulting chromosomal loss and gain was illustrated in heatmaps using Matlab. Most of the samples showed noisy profiles, likely due to DNA breakage accumulated during the storage in FFPE, and the analysis could not be completed for all samples.
Euclidean distances were calculated between any two samples using all 396,020 filtered probes. Averaged normal prostate and normal lymph node samples showed minimal variance and were used for the analysis. Normal prostate was considered to be very similar because only 0.65% (2561/396020) of standard deviations of all the probes were above 0.15. Normal lymph nodes were considered to be very similar because only 0.98% (3875/396020) of standard deviations of all the probes were above 0.15. The primary focus with the shortest Euclidean distance to the lymph node metastasis (T-PL dist 1) was categorized as aggressive. The additional distance to the other primary foci (T-PL dist 2; actual T-PL dist-T-PL dist 1=T-PL dist 2) were assessed in a density graph and a division of the scale based hereon. If T-PL dist 2s were only 0-10 units longer, they were also categorized as aggressive. This ensured that the foci of monoclonal origin would all be grouped as aggressive. Next, T-PL dist 2s longer by >20 units were categorized as non-aggressive origins and T-PL dist 2s of between 10-20 were categorized as undecided (overview in Table 2). In the two patients with 2 PLs the division of the primary tumors was done based on the PL with the shortest distance to a primary focus, which was P23_PL2 and P56_PL1.
Methylation-based phylogenetic trees were inferred by the minimal evolution method47. Euclidean distances were calculated using all 396,020 filtered probes.
Differential methylation between any two groups of samples were calculated using the champ.MVP( ) function from the ChAMP package utilizing either FDR<0.05 or FDR<0.3.
By combining the categorized samples into groups of aggressive (n=31) and non-aggressive (n=10), we generated a list of 3,000 most variably methylated CpG sites (probes) between the groups. This list was used as input for the GLMnet algorithm35, along with normal (n=12), non-aggressive (n=10), and aggressive (n=31) prostate sample groups. The GLMnet algorithm outputs a set of probes able to differentiate groups of samples based on their DNA methylation. Following 15 iterations, each output was evaluated by 1) the separation of the three groups (input as normal, aggressive, non-aggressive) in MDS plots like in
The HM450 methylation data for the prostate adenocarcinoma samples from TCGA project was downloaded from the TCGA Data Portal (https://tcga-data.nci.nih.gov/tcga/). After filtering samples based on the same criteria as for our own samples, 499 samples (45 normal, 453 tumor, and 1 metastatic) remained. After removing samples with missing values among the 25 predictor probes, 496 samples remained (45 normal, 450 tumor, and 1 metastatic). The classifier was run on these samples and 70% were predicted with a probability above a cutoff of 0.67. A cutoff of 0.67 was chosen because as a consequence the probability for either of the two other groups must be 0.33 or less. Clinicopathological data was available for most samples in Biotab-files and are shown for the samples predicted above the 0.67 cutoff.
In
Methylation array data have been submitted to the NCBI Gene Expression Obnibus (GEO; www.ncbi.nlm.nih.gov/geo/) under accession number GSE73549.
Identification of PC aggressiveness is fundamental to improving clinical decision-making in patients diagnosed with organ-confined PC regarding treatment or active surveillance. By implementing our design of examining DNA methylation in primary multifocal PC and matched lymph node metastases, we were able to examine the relationships amongst primary foci as well as the relationships between primary foci and metastases. Importantly, we found that more than half of the patients in our cohort showed multiple subclones, findings similar to previously reported studies9,11-14,16-18, and also that DNA methylation of a lymph node metastasis is similar to a cancerous focus/foci from the same patient. Taking advantage of these findings, we developed a method to categorize the subclonal relationship and aggressiveness of individual PC foci. The resulting aggressive and non-aggressive sample groups, along with adjacent-normal samples, were used to search for biomarkers to distinguish the three groups, and the outcome was a 25-probe aggressiveness classifier. The classifier showed prognostic value when it was applied to samples from the PC cohort from TCGA.
For this study, we relied on the assumption that DNA methylation can inform on clonal evolution. Several studies have addressed the connection between DNA methylation and clonal evolution with high precision11,27,28 and recently, Costello and colleagues reported that phyloepigenetic relationships robustly recapitulate phylogenetic patterns in gliomas and their recurrences29. Two or more foci originated from the same subclone in 11 of 14 patients in our cohort (
While clinical tools and techniques have improved immensely2,4,36-39, the determination of tumor aggressiveness prior to physical manifestation must rely on biomarkers measured biochemically or at a molecular level. One impediment to success is how to define tumor aggressiveness with respect to a clinical end point. Often GS or time to PSA recurrence is used as a surrogate for PC aggressiveness, which would be more appropriately evaluated using metastatic progression or mortality. In this study, we used a novel approach in defining aggressiveness as the ability to give rise to lymph node metastases. The presence of lymph node metastases is an indication of tumor cells having acquired the ability to leave the primary site and proliferate in a secondary site, and thus acts as an indicator for the capacity of the cancer to establish distant metastases. In addition to this type of lymphatic dissemination, metastases can also arise through hematogenous dissemination to brain, lungs, liver, and bone marrow48. Secondary cancer growths at these sites are not routinely removed during treatment for metastatic PC, and thus, the tissue for research is not available until postmortem. Although we recognize that distant metastases do not exclusively arise through lymphatic dissemination, we show that this clinical end point is very relevant alone or in concert with other clinicopathological parameters (
Gleason score 7 (GS 7) tumors are among the most difficult and poorly established backgrounds for making clinical decisions49,50, however, our study demonstrated that aggressiveness of PCs with GS 7 using our classifier is highly correlated with pathological tumor stage, but not specific for primary or secondary Gleason patterns (4+3 or 3+4,
A limitation to the presented study is that our discovery set is effectively only 14 patients, from whom we have 79 total samples. A larger discovery set would improve the study and would probably result in an enlargement of the classifier to more than 25 probes due to the vast PC heterogeneity51. Despite the modest size of the discovery set, we were able to validate the aggressiveness classifier, and thus, our study approach using publicly available TCGA prostate adenocarcinoma DNA methylation data from 496 primary tissues. Upon correlating our predictions with the TCGA clinicopathological information, we found a significant association (P<0.02) between aggressiveness and pre-operative PSA levels, pathological GS, presence of lymph node metastases and tumor stage, but interestingly, we did not find any correlation with tumor size. We do recognize that different clinical endpoints would be better suited to describe poor clinical outcome, however, but regret that the average follow-up period of the TCGA PRAD cohort was only 3.16 years. As a result, we found that too few patients had recurred and thus only found a significant difference between the groups for tumor status. Taken together, the presented data indicate the importance of using DNA methylation data to identify aggressive lesions more specifically than any currently used approach. This approach has clinical applications for early detection in PC biopsy specimens.
Upon suspicion of PC, prostate biopsies are performed as the standard-of-care method for PC diagnosis40. Currently, prostate needle biopsies are most commonly performed trans-rectally in a systematic, yet random format. This systematic, random biopsy strategy has a high rate of misdiagnoses, since the non-targeted needles may either miss the clinically significant cancer focus, capture only a clinically insignificant cancer focus, or completely miss all cancer foci20,41. Thus, the significant sampling error of traditional systematic, random prostate biopsies renders them unreliable for accurate characterization of index tumor location, volume, and GS41. The recently developed image-guided targeted prostate biopsy technique, which fuses magnetic resonance and three-dimensional transrectal ultrasound images, can reliably identify the location and the primary Gleason pattern of index lesions38,39. By combining image-guided targeted biopsies and our DNA methylation classifier (following further clinical validation), the ability to identify aggressive focus, and subsequently characterize biopsy-detected PC foci more accurately should be enhanced. The ability to determine aggressiveness in a biopsy sample mapped to a particular prostate location should lead to the ability to make more informed clinical decisions regarding the choice between active surveillance of non-aggressive PC foci and surgery or targeted focal ablation therapy of the aggressive PC foci. Initially, the aggressiveness classifier should be developed into a more cost- and labor efficient test in the form of a custom DNA methylation array or multiplexed PCR-based assay (MSP or MethyLight)42,52.
Although the present invention has been described in terms of specific exemplary embodiments and examples, it will be appreciated that the embodiments disclosed herein are for illustrative purposes only and various modifications and alterations might be made by those skilled in the art without departing from the spirit and scope of the invention as set forth in the following claims.
The following references are each relied upon and incorporated herein in their entirety.
This application is a Continuation Application of U.S. application Ser. No. 15/999,311 filed Aug. 17, 2018, now pending, which is a 35 USC § 371 National Stage application of International Application No. PCT/US2017/018517 filed Feb. 17, 2017, now pending; which claims the benefit under 35 USC § 119(e) to U.S. Application Ser. No. 62/296,707 filed Feb. 18, 2016, now expired. The disclosure of each of the prior applications is considered part of and is incorporated by reference in the disclosure of this application.
| Number | Date | Country | |
|---|---|---|---|
| 62296707 | Feb 2016 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 15999311 | Aug 2018 | US |
| Child | 17089491 | US |
