Patent Application
20190112672
The present disclosure relates generally to a prostate cancer biomarker signature. More particularly, the present disclosure relates to a mitochondrial DNA for the prognosis of prostate cancer outcomes, which can inform treatment decisions and guide therapy.
Prostate cancer remains the most prevalent non-skin cancer in men1 and exhibits a remarkably quiet mutational profile2. Exome sequencing studies of localized tumours have revealed few recurrent somatic single nucleotide variants (SNVs)3,4, while whole-genome sequencing studies have not identified highly recurrent driver non-coding SNVs or genomic rearrangements (GRs)5-8. Although strong mutagenic field effects have been observed9,10, their underlying mechanisms and to what extent they drive tumour initiation or progression are unknown. Nevertheless, promising molecular diagnostics predictive of aggressive disease have been created using supervised machine-learning techniques, both from RNA abundance data11,12 and from DNA copy number data13, showing strong linkage between molecular features of prostate tumour cells and patient outcome.
Most studies of the prostate cancer genome have focused on mutations occurring in the nuclear genome, and have ignored the other genome of the cell: the mitochondrial genome. Mitochondria are maternally inherited and play critical roles in pathways dysregulated in cancer cells, including energy production, metabolism and apoptosis14. While mitochondrial mutations have been observed in several tumour types15-17, including prostate cancer18-22, their global frequency and clinical impact have not yet been comprehensively characterized. Previous studies have found that mitochondrial mutations are associated with increased serum prostate-specific antigen (PSA) levels21, have suggested that mtDNA mutations increase cancer cell tumourigenicity20, and indicate that overall mitochondrial mutation burden is correlated with higher Gleason Scores22.
In an aspect, there is provided a method of prognosing and/or predicting disease progression and/or in subject with prostate cancer, the method comprising: a) providing a sample containing mitochondrial genetic material from prostate cancer cells; b) sequencing the mitochondrial genetic material with respect to at least 1 patient biomarker selected from CSB1, OHR, ATP8 and HV1 (hypervariable region 1); c) comparing the sequence of said patient biomarkers to control or reference biomarkers to determine mitochondrial single nucleotide variations (mtSNVs); and d) determining the a prostate cancer prognosis; wherein a relatively worse outcome is associated with the presence of mtSNVs in CSB1, OHR, ATP8 and a relatively better outcome is associated with the presence of mtSNVs in HV1.
In an aspect, there is provided a computer-implemented method of prognosing or predicting disease progression in a patient with prostate cancer, the method comprising: a) receiving, at at least one processor, sequencing data of mitochondrial genetic material from prostate cancer cells of the patient, the sequencing data reflecting at least 1 patient biomarker selected from CSB1, OHR, ATP8 and HV1 (hypervariable region 1); b) comparing, at the at least one processor, said sequencing data to corresponding control or reference sequences to determine mitochondrial single nucleotide variations (mtSNVs); d) determining, at the at least one processor, a prostate cancer prognosis; wherein a relatively worse outcome is associated with the presence of mtSNVs in CSB1, OHR, ATP8 and a relatively better outcome is associated with the presence of mtSNVs in HV1.
In an aspect, there is provided a computer program product for use in conjunction with a general-purpose computer having a processor and a memory connected to the processor, the computer program product comprising a computer readable storage medium having a computer mechanism encoded thereon, wherein the computer program mechanism may be loaded into the memory of the computer and cause the computer to carry out the method described herein.
In an aspect, there is provided a computer readable medium having stored thereon a data structure for storing the computer program product described herein.
In an aspect, there is provided a device for prognosing or predicting disease progression in a patient with prostate cancer, the device comprising: at least one processor; and electronic memory in communication with the at one processor, the electronic memory storing processor-executable code that, when executed at the at least one processor, causes the at least one processor to: a) receive sequencing data of mitochondrial genetic material from prostate cancer cells of the patient, the sequencing data reflecting at least 1 patient biomarker selected from CSB1, OHR, ATP8 and HV1 (hypervariable region 1); b) compare said sequencing data to corresponding control or reference sequences to determine mitochondrial single nucleotide variations (mtSNVs); and c) determining, at the at least one processor, a prostate cancer prognosis; wherein a relatively worse outcome is associated with the presence of mtSNVs in CSB1, OHR, ATP8 and a relatively better outcome is associated with the presence of mtSNVs in HV1. In some embodiments, the processor further displays the prostate cancer prognosis on a user display.
In an aspect, there is provided a kit for prognosing or predicting disease progression in a patient with prostate cancer, the kit comprising primer sequences that permit the sequencing of a mitochondrial genome to determine mtSNVs in ATP8, OHR, ND4L and CSB1.
Other aspects and features of the present disclosure will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments in conjunction with the accompanying figures.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the attached Figures and Tables.
Table 1 shows results of PCR validation of 25 mtSNVs. The table includes the mtSNV position, which PCR primers were used to validate, the heteroplasmy fraction (adjusted by cellularity) of the major allele for both tumour and normal and the results of the PCR amplification and Sanger sequencing.
Table 2 shows results from univariate Cox proportional modeling. Hazard ratios were calculated for the different mitochondrial loci individually, the table includes the HR and 95% CI, p-values, the change in 10 year survival and the number of patients with a mtSNV in that loci.
Table 3 shows the sequence and mtDNA targeted region of 20 forward and reverse PCR primers.
Table 4 shows clinical and sequencing data per patient. The data includes patient age at treatment, Gleason Score, T-category, PSA (ng/mL) level, tumour cellularity, number of mtSNVs and the mean coverage depth, mitochondrial copy number for both normal and tumour sample and the aligner used for each wgs. The presence or absence of mutations in each of 20 mitochondrial regions and MYC and NKX3-1 copy number aberrations is indicated for each sample and the amount of DNA that was sent for sequencing for the CPC-GENE samples are included.
Table 5 shows 293 somatic mtSNVs. List of mtSNVs, including heteroplasmic fractions (HF), reference allele nucleotide, identity of tumour and normal major alleles and major allele heteroplasmy fractions (both adjusted and unadjusted by tumour cellularity), tumour and normal coverage at each position, the mtDNA gene or region and pathogenicity scores from MutPred and Polyphen2 obtained from MToolBox.
Table 6 shows mitochondrial mutation recurrence for 41 nuclear genomic features. The table includes the number of patients that had a specific nuclear genome CNA, GR, methylation event or SNV and of those patients the number that also harbours an mtSNV in any of 22 mtDNA features.
Table 7 shows mtSNVs with ΔHF values between 0.1 and 0.2. List of 265 mtSNVs, that had ΔHF values greater than 0.1, but less than 0.2. The table includes heteroplasmic frequencies, reference allele nucleotide, identity of tumour and normal major alleles and major allele heteroplasmy fractions, tumour and normal coverage at each position and the mtDNA gene or region.
Nuclear mutations are well-known to drive tumour incidence, aggression and response to therapy. By contrast, the frequency and roles of mutations in the maternally-inherited mitochondrial genome are poorly understood. To characterize the mitochondrial mutation landscape of prostate cancer, we analyzed the mitochondrial genomes of 384 adenocarcinomas of the prostate across all National Comprehensive Cancer Network (NCCN) defined risk categories, including 164 early-onset prostate cancers (EOPCs, age at diagnosis less than 50). We identified a median of one mitochondrial single nucleotide variant (mtSNV) per patient.
We identify recurrent mutational hotspots in the mitochondria! genome, which included recurrently mutated bases or recurrently mutated genes or regions. We also confirm increasing mutation burden with patient age23-26, identify interactions between nuclear and mitochondrial mutation profiles and reveal specific mitochondrial mutations enriched in aggressive prostate tumours. For example certain control region mtSNVs co-occur with gain of the MYC oncogene, and these mutations are jointly associated with patient survival.
These data demonstrate frequent mitochondrial mutation in prostate cancer, and suggest interplay between nuclear and mitochondrial mutational profiles in prostate cancer.
The methods described herein are useful for prognosing the outcome of a subject that has, or has had, a cancer associated with the prostate. The cancer may be prostate cancer or a cancer that has metastasized from a cancer of the prostate.
In an aspect, there is provided a method of prognosing and/or predicting disease progression and/or in subject with prostate cancer, the method comprising: a) providing a sample containing mitochondrial genetic material from prostate cancer cells; b) sequencing the mitochondrial genetic material with respect to at least 1 patient biomarker selected from CSB1, OHR, ATP8 and HV1 (hypervariable region 1); c) comparing the sequence of said patient biomarkers to control or reference biomarkers to determine mitochondrial single nucleotide variations (mtSNVs); and d) determining the a prostate cancer prognosis; wherein a relatively worse outcome is associated with the presence of mtSNVs in CSB1, OHR, ATP8 and a relatively better outcome is associated with the presence of mtSNVs in HV1.
The term “subject” as used herein refers to any member of the animal kingdom, preferably a human being and most preferably a human being that has, has had, or is suspected of having prostate cancer.
The term “sample” as used herein refers to any fluid (e.g. blood, urine, semen), cell, tumor or tissue sample from a subject which can be assayed for the biomarkers described herein.
The term “genetic material” used herein refers to materials found/originate in the nucleus, mitochondria and cytoplasm, which play a fundamental role in determining the structure and nature of cell substances, and capable of self-propagating and variation. In the context of the present methods, the genetic material is any material from which one can measure the biomakers described herein. The genetic material is preferably DNA.
The term “prognosis” as used herein refers to the prediction of a clinical outcome associated with a disease subtype which is reflected by a reference profile such as a biomarker reference profile. The prognosis provides an indication of disease progression and includes an indication of likelihood of death due to cancer. The prognosis may be a prediction of metastasis, or alternatively disease recurrence. In one embodiment the clinical outcome class includes a better survival group and a worse survival group. The term “prognosing or classifying” as used herein means predicting or identifying the clinical outcome of a subject according to the subject's similarity to a reference profile or biomarker associated with the prognosis. For example, prognosing or classifying comprises a method or process of determining whether an individual has a better or worse survival outcome, or grouping individuals into a better survival group or a worse survival group, or predicting whether or not an individual will respond to therapy.
The term “biomarker profile” as used herein refers to a dataset representing the state or expression level(s) of one or more biomarkers. A biomarker profile may represent one subject, or alternatively a consolidated dataset of a cohort of subjects, for example to establish a reference biomarker profile as a control.
As used herein, the term “control” refers to a specific value or dataset that can be used to prognose or classify the value e.g the measured biomarker or reference biomarker profile obtained from the test sample associated with an outcome. In one embodiment, a dataset may be obtained from samples from a group of subjects known to have cancer having different tumor states and/or healthy individuals. The state or expression data of the biomarkers in the dataset can be used to create a control value that is used in testing samples from new patients. In some embodiments, a cohort of subjects is used to obtain a control dataset. A control cohort patients may be a group of individuals with or without cancer. In a particularly embodiment, the control is a patient's own matched normal profile (e.g. from blood or normal tissue).
As used herein, “overall survival” refers to the percentage of or length of time that people in a study or treatment group are still alive following from either the date of diagnosis or the start of treatment for a disease, such as cancer. In a clinical trial, measuring the overall survival is one way to see how well a new treatment works.
As used herein, “relapse-free survival” refers to, in the case of caner, the percentage of or length of time that people in a study or treatment group survive without any signs or symptoms of that cancer after primary treatment for that cancer. In a clinical trial, measuring the relapse-free survival is one way to see how well a new treatment works. It is defined as any disease recurrence or relapse (local, regional, or distant).
The term “good survival” or “better survival” as used herein refers to an increased chance of survival as compared to patients in the “poor survival” group. For example, the biomarkers of the application can prognose or classify patients into a “good survival group”. These patients are at a lower risk of death after surgery and can also be categorized into a “low-risk group”.
The term “poor survival” or “worse survival” as used herein refers to an increased risk of disease progression or death as compared to patients in the “good survival” group. For example, biomarkers or genes of the application can prognose or classify patients into a “poor survival group”. These patients are at greater risk of death or adverse reaction from disease or surgery, treatment for the disease or other causes, and can also be categorized into a “high-risk group”.
A person skilled in the art would understand how to implement differing cut-offs for good survival vs. worse survival, depending on the clinical outcome one is predicting and the biomarkers being assayed.
In some embodiments, the at least 1 patient biomarker, is at least 2, 3 or 4 patient biomarkers.
In some embodiments, the prostate cancer is localized prostate cancer, preferably non-indolent localized prostate cancer.
In some embodiments, the method further comprises building a patient biomarker profile from the determined or measured patient biomarkers.
In some embodiments, the prostate cancer prognosis is the likelihood of disease recurrence, preferably measured by biochemical relapse.
In some embodiments, the method further comprises classifying the patient into a high risk group if the likelihood of disease recurrence is relatively high or a low risk group if the likelihood of disease recurrence is relatively low.
In some embodiments, the method further comprises treating the patient with more aggressive therapy if the patient is in the high risk group. Preferably, the more aggressive therapy comprises adjuvant therapy, preferably hormone therapy, chemotherapy or radiotherapy.
In some embodiments, the patient biomarkers further comprise 002, CO3 and ND4L. Preferably, the at least 1 biomarker is at least 5, 6 or all 7 biomarkers. Further preferably, the at least 1 biomarker is all 7 biomarkers.
In some embodiments, the subject is classified as low risk if there exists mtSNVs in CO2, CO3, and HV1 and high risk if there exists mtSNVs in ATPS, OHR, ND4L and CSB1.
In some embodiments, the mtSNVs are the mtSNVs identified in Table 5. [NTD: Please confirm]
The present system and method may be practiced in various embodiments. A suitably configured computer device, and associated communications networks, devices, software and firmware may provide a platform for enabling one or more embodiments as described above. By way of example,
The present system and method may be practiced on virtually any manner of computer device including a desktop computer, laptop computer, tablet computer or wireless handheld. The present system and method may also be implemented as a computer-readable/useable medium that includes computer program code to enable one or more computer devices to implement each of the various process steps in a method in accordance with the present invention. In case of more than computer devices performing the entire operation, the computer devices are networked to distribute the various steps of the operation. It is understood that the terms computer-readable medium or computer useable medium comprises one or more of any type of physical embodiment of the program code. In particular, the computer-readable/useable medium can comprise program code embodied on one or more portable storage articles of manufacture (e.g. an optical disc, a magnetic disk, a tape, etc.), on one or more data storage portioned of a computing device, such as memory associated with a computer and/or a storage system.
In an aspect, there is provided a computer-implemented method of prognosing or predicting disease progression in a patient with prostate cancer, the method comprising: a) receiving, at at least one processor, sequencing data of mitochondrial genetic material from prostate cancer cells of the patient, the sequencing data reflecting at least 1 patient biomarker selected from CSB1, OHR, ATP8 and HV1 (hypervariable region 1); b) comparing, at the at least one processor, said sequencing data to corresponding control or reference sequences to determine mitochondrial single nucleotide variations (mtSNVs); d) determining, at the at least one processor, a prostate cancer prognosis; wherein a relatively worse outcome is associated with the presence of mtSNVs in CSB1, OHR, ATP8 and a relatively better outcome is associated with the presence of mtSNVs in HV1.
In some embodiments, the method further comprises displaying the prostate cancer prognosis on a user display.
In an aspect, there is provided a computer program product for use in conjunction with a general-purpose computer having a processor and a memory connected to the processor, the computer program product comprising a computer readable storage medium having a computer mechanism encoded thereon, wherein the computer program mechanism may be loaded into the memory of the computer and cause the computer to carry out the method described herein.
In an aspect, there is provided a computer readable medium having stored thereon a data structure for storing the computer program product described herein.
In an aspect, there is provided a device for prognosing or predicting disease progression in a patient with prostate cancer, the device comprising: at least one processor; and electronic memory in communication with the at one processor, the electronic memory storing processor-executable code that, when executed at the at least one processor, causes the at least one processor to: a) receive sequencing data of mitochondrial genetic material from prostate cancer cells of the patient, the sequencing data reflecting at least 1 patient biomarker selected from CSB1, OHR, ATP8 and HV1 (hypervariable region 1); b) compare said sequencing data to corresponding control or reference sequences to determine mitochondrial single nucleotide variations (mtSNVs); and c) determining, at the at least one processor, a prostate cancer prognosis; wherein a relatively worse outcome is associated with the presence of mtSNVs in CSB1, OHR, ATP8 and a relatively better outcome is associated with the presence of mtSNVs in HV1. In some embodiments, the processor further displays the prostate cancer prognosis on a user display.
As used herein, “processor” may be any type of processor, such as, for example, any type of general-purpose microprocessor or microcontroller (e.g., an Intel™ x86, PowerPC™, ARM™ processor, or the like), a digital signal processing (DSP) processor, an integrated circuit, a field programmable gate array (FPGA), or any combination thereof.
As used herein “memory” may include a suitable combination of any type of computer memory that is located either internally or externally such as, for example, random-access memory (RAM), read-only memory (ROM), compact disc read-only memory (CDROM), electro-optical memory, magneto-optical memory, erasable programmable read-only memory (EPROM), and electrically-erasable programmable read-only memory (EEPROM), or the like. Portions of memory 102 may be organized using a conventional filesystem, controlled and administered by an operating system governing overall operation of a device.
As used herein, “computer readable storage medium” (also referred to as a machine-readable medium, a processor-readable medium, or a computer usable medium having a computer-readable program code embodied therein) is a medium capable of storing data in a format readable by a computer or machine. The machine-readable medium can be any suitable tangible, non-transitory medium, including magnetic, optical, or electrical storage medium including a diskette, compact disk read only memory (CD-ROM), memory device (volatile or non-volatile), or similar storage mechanism. The computer readable storage medium can contain various sets of instructions, code sequences, configuration information, or other data, which, when executed, cause a processor to perform steps in a method according to an embodiment of the disclosure. Those of ordinary skill in the art will appreciate that other instructions and operations necessary to implement the described implementations can also be stored on the computer readable storage medium. The instructions stored on the computer readable storage medium can be executed by a processor or other suitable processing device, and can interface with circuitry to perform the described tasks.
As used herein, “data structure” a particular way of organizing data in a computer so that it can be used efficiently. Data structures can implement one or more particular abstract data types (ADT), which specify the operations that can be performed on a data structure and the computational complexity of those operations. In comparison, a data structure is a concrete implementation of the specification provided by an ADT.
In an aspect, there is provided a kit for prognosing or predicting disease progression in a patient with prostate cancer, the kit comprising primer sequences that permit the sequencing of a mitochondrial genome to determine mtSNVs in ATPS, OHR, ND4L and CSB1.
In some embodiments, the primers further permit sequencing of CO2, CO3 and ND4L.
The above listed aspects and/or embodiments may be combined in various combinations as appreciated by a person of skill in the art. The advantages of the present disclosure are further illustrated by the following examples. The examples and their particular details set forth herein are presented for illustration only and should not be construed as a limitation on the claims of the present invention.
Methods/Materials
Patient Cohort
We collected 384 prostate cancer tumour samples with matched normal samples (381 blood, 3 tissue-derived). The samples had Gleason Scores ranging from 3+3 to 5+4. The 165 patients from the Canadian Prostate Cancer Genome Network (CPC-GENE) underwent either radical prostatectomy or image-guided radiotherapy as detailed in Fraser et al. (2017)7. In addition, 51 samples from publicly available datasets were included in the somatic mutation analysis and correlations with clinical variables, age, Gleason Score and T-category4-6,8, three of TOGA samples had tissue-derived normal samples as opposed to blood-normals. All samples were manually macro-dissected and were assessed by an expert urological pathologist to have tumour cellularity >70%. All tumour specimens were taken from the index lesion. Publicly available tumour tissues were obtained and used following University Health Network Research Ethics Board (REB) approved study protocols (UHN 06-0822-CE, UHN 11-0024-CE, CHUQ 2012-913:H12-03-192). Local REB and ICGC guidelines were used to collect whole blood and informed consent from CPC-GENE patients at the time of clinical follow-up.
EOPC Patient Cohort and Sample Processing
We collected 168 tumour samples from EOPC patients. Informed consent and an ethical vote (institutional reviewing board) were obtained according to the current ICGC guidelines. The patients did not receive any neo-adjuvant radiotherapy, androgen deprivation therapy, or chemotherapy prior to the surgical removal of tumor tissue. Tumor samples and a normal blood control were frozen at −20° C. and subsequently stored at −80° C.
EOPC DNA Library Preparation, Sequencing and Alignment
DNA library preparation and whole-genome sequencing was performed on Illumina sequencers with the raw length of the reads displaying a median of 101 bp. Reads were aligned to the hg19 reference genome using BWA-MEM version 0.7.8-r455 [arXiv:1303.3997v2] and duplicates were removed using Picard (http://broadinstitute.github.io/picard). Mitochondrial reads were extracted using SAMtools39.
Nuclear Mutation Calling
Recurrent nuclear genomic features were obtained from Fraser et al. (2017)7, which included five recurrent coding SNVs from commonly mutated genes in prostate cancer; the six most recurrent noncoding SNVs; CNAs from eight commonly mutated prostate cancer genes; the 10 GRs included the five most recurrent translocations and the four most recurrent inversions plus a recurrent inversion containing the PTEN gene; the TMPRSS-ERG fusion; presence or absence of kataegis events; chromothripsis; 3 metrics of mutation density (median dichotomized PGA estimates, number of SNVs and number of GRs); six methylation events were identified through univariate CoxPH modelling as associated with disease progression. Nuclear somatic single nucleotide variants were predicted by SomaticSniper (v1.0.2)38, (n=172 samples) setting the mapping quality threshold to 1, otherwise with default parameters. Nuclear SNVs were filtered using SAMtools (v0.1.6)39 and SomaticSniper (v1.0.2) provided filters, as well as a mapping quality filter and false positive filter from bam-readcount (downloaded Jan. 10, 2014). Nuclear SNVs were then annotated by ANNOVAR (v2015-06-17)40. The nuclear mutation rate was obtained by dividing the number of SNVs after filtering by the number of callable loci. Copy number aberrations were analyzed by Affymetrix OncoScan microarrays (n=194) and methylation data was generated by Illumina Infinium Human Methylation 450k BeadChip kits (n=104). Genomic rearrangements were called using Delly (v0.5.5)41 (n=172). Chromothripsis scores (n=159) were calculated by ShatterProof (v0.14)42 and subsequently dichotomized with a 0.517 threshold. Sample processing, whole genome sequencing and whole genome sequencing data analysis are as described in detail by Fraser et al. (2017)7.
Mitochondrial SNV Calling
Reads mapped to the mitochondria during whole genome alignment were extracted using BAMQL (v1.1)43 using the command:
bamql -I -o out_mito_reads.bam -f input_wgs.bam ‘(chr(M) & mate_chr(M)) |(chr(Y) & after(59000000) & mate_chr(M))’;
The second part of the query statement collects reads where one of the pair mapped to chrM and the other unmapped which in our data was also assigned to an unresolved region in chrY.
The output files from BAMQL were used as input barn files for the mitochondrial genome analysis program MToolBox (v0.2.2)44. The versions of the various system requirements were: Python v2.7.2; gmap v.2013-07-2045; samtools v0.1.1839; java v1.7.0_72; picard v1.92 (http://broadinstitute.github.io/picard); muscle v3.8.3146. We used default parameters for MToolBox and used the default RSRS47 as the reference genome. The default parameters include a minimum base quality score of 25, samples that failed the MToolBox program using default parameters, but successfully completed at a lower base quality parameter setting of 20, were nonetheless removed from the analysis.
MToolBox_v0.2.2/MToolBox.sh bam -r RSRS -M -I -m′-D genome_index/-H hg19RSRS -M chrRSRS′-a′-r genome_fasta/-F -P -C′
The predicted mitochondrial genome for each tumour sample and the number of reads supporting each base were compared to the corresponding normal sample if available, from each patient. Positions where the absolute difference in heteroplasmy fraction (ΔHF) was greater than 0.2 were considered to be mitochondrial SNVs (mtSNVs). While this does not preclude the possibility of tissue-specific heteroplasmy being mislabeled as somatic mutations, this allowed us to identify somatic variants as well as ignore those positions that could be called population variants, reducing the number of potentially false positive variant calls. Heteroplasmy fraction estimates were adjusted to account for tumour cellularity using cellularity values calculated by qpure48. Tumour HF values were adjusted with the following equation:
Tumour HFcellularity=(Tumour HFMToolBox−(1−cellularity)*Normal HFMToolBox)/cellularity
If there were no cellularity values available we assumed cellularity=1.0. Those values of Tumour HFcellularity that were less than zero or greater than one were rounded to zero and one respectively.
In the mitochondrial reference genome there are three positions encoded as ‘N’ to preserve historical numbering, (523, 524 and 3107), in addition position 310 is located within a homopolymer region and is a common variant28. These four positions can result in misalignments49, therefore they were filtered out of our analyses, as in previous studies50. We also filtered out those positions with relatively low coverage of less than 100 read depth. Positions of mitochondrial genes and subregions of the noncoding control region were obtained from http://www.mitomap.orq. Pathogenicity scores from MutPred51, PolyPhen-252 and SiteVar53 were obtained from the MToolBox output. Mutations in tRNA genes were compared to the Mamit-tRNA database54.
We chose to a threshold of 0.2 ΔHF in order to balance removing false positives without excluding a large number of mtSNVs unnecessarily (
mtDNA Copy Number
Mitochondrial copy number per cell (MCN) was calculated using the equation: (mitochondrial coverage/nuclear coverage) ×2, using nuclear coverage data from the whole genome alignment7 and mitochondrial coverage data calculated by bedtools genomecov (v2.24.0)55. The mitochondrial mutation rate per megabase DNA was calculated by dividing the number of mtSNVs by the tumour MCN multiplied by the number of callable bases, 16565, accounting for the 4 positions that were removed.
Survival and Statistical Analyses
The mtSNV data were compared to patient clinical features in the R statistical environment (v3.2.3). Binomial regression (age, PSA) and Chi-square tests (T-category, Gleason Score) were used to identify associations between the clinical variables and mtSNVs for all 384 patients. Survival analyses were performed on 165 patients due to survival data availability. Cox proportional hazards models were used to calculate HRs for mtSNVs in the different mitochondrial features such as genes or MCN, with verification of the proportional hazards assumption. The mitochondrial feature MT-ND4L was removed from this analysis as only one patient in the 165 cohort had a mtSNV in this gene. Change in 10 year percent survival was calculated using survival rates. Kaplan-Meier plots were created comparing biochemical recurrence with the presence or absence of mutations in certain mitochondrial loci, (genes or noncoding regions) or median-dichotomized tumour MCN. Nuclear genomic features were chosen based on recurrence in a previous prostate cancer study7. Data was visualized using the R-environment and lattice (v0.20-31), latticeExtra (v0.6-26) and circos (v0.67-4)56. Associations between nuclear and mitochondrial genome features were calculated using Spearman's correlation.
PCR Validation
Single nucleotide variants in mitochondrial DNA were validated by Sanger re-sequencing, as previously reported7. Briefly, 10 ng of total genomic DNA (including mitochondrial DNA) was subjected to PCR amplification using primer pairs flanking SNVs identified from whole-genome sequencing (Table 3). Sequence data surrounding the region of interest was obtained from http://www.mitomap.orq/bin/view.pl/MITOMAP/HumanMitoSeq. The amplicon sequence generated by the in silico PCR was then entered into the NCBI genome BLAST search engine to identify non-mitochondrial sequences that were similar. This was done to ensure that there were some differences between the designed primers and nuclear sequences, as well as to identify any sequence regions that could confound downstream analyses. The genome used for the BLAST search was GRCh38.p2 reference assembly top-level. These web pages were used on Aug. 20 and 21, 2015 and verified on Sep. 13, 2016. PCR reactions were purified using the QIAquick PCR purification kit (Qiagen, Toronto, Canada). Sanger re-sequencing was performed using amplicon-specific primers on an ABI 3730XL capillary electrophoresis instrument (Thermo Fisher Scientific, Burlington, Canada) at The Centre for Applied Genomics, Hospital for Sick Children, Toronto, Canada.
Data Availability Statement
Sequencing data is available at the European Genotype-Phenotype Archive (EGA) repository under accession EGAS00001001782.
Results
Mitochondrial Genome Sequence Analysis
We collected 384 tumours from patients with localized prostate cancer, comprising 164 EOPCs and 220 late-onset prostate cancers (LOPC; Table 4;
We next conservatively identified mitochondrial SNVs (mtSNVs) as those positions that had an absolute difference in their heteroplasmy fraction (ΔHF) between purity-adjusted tumour and paired-normal samples of at least 0.20 (
Frequently Mutated Mitochondrial Loci
The noncoding control region of the mitochondria (mtDNA positions: 1-576 and 16024-16569), was the most frequently mutated region with 15.4% (59/384) of tumours harbouring mutations in that region (Table 5;
There were 157 mtSNVs in the 13 protein coding genes, 82% (129/157) of which were nonsynonymous, including 6 premature stop codons and two mutated stop codons. The most frequently mutated protein coding gene was ND5 (30/157). We identified 21 specific positions mutated in at least two patients (
There were 22 mutations within mitochondrial tRNA genes, and eight of these were located within anticodon stems. In CO1 there were non-synonymous mutations at G5910A (A2T in one patient; ΔHF: 0.84) and T6664C (1254T in one patient; ΔHF: 0.46), two amino acids previously observed to be mutated in prostate cancer cells20. Two patients with mutations at position 6419 were detected within the CO1 gene (ΔHF: 0.2, 0.23), although these two showed heteroplasmy within the normal samples and homoplasmy in the tumour suggesting that these mtSNVs represent either tissue-specific heteroplasmy32 or mutations that have gone to fixation in the tumour. Overall CO1 was mutated in 4.7% (18/384) of patients, markedly lower than the 11% rate previously reported20.
Age Effect on the Distribution of mtSNVs in Prostate Cancer
As expected, the occurrence of mitochondrial mutations was strongly associated with patient age (GLM family binomial; p=5.88×10−9;
Associations Between mtSNVs and Nuclear Genomic Mutations
Intriguingly, mutations in the large rRNA subunit (RNR2) were significantly correlated with mutations in the mitochondrial gene ND4 (Spearman's p=0.19; p=0.00015), suggesting to us an inter-play between different mutational types. To rigorously assess this phenomenon, we studied mutational associations between the nuclear and mitochondrial genomes. We exploited a set of 40 candidate nuclear somatic driver events recently identified through recurrence analyses, including five measures of mutation density, six methylation events, six non-coding SNVs, five coding SNVs, five measures of mutational density, ten genomic rearrangements and eight copy number aberrations (CNAs)7. The SNVs included recurrent coding SNVs in genes that are commonly mutated in prostate cancer, as well as the six most recurrent non-coding SNVs. To characterize per-region mtSNVs, we defined 22 mutational features representing the broad functional aspects of the mitochondria, 13 protein coding genes, 2 rRNAs, tRNAs (treated as one group), the control region and 3 subregions within the control region, along with mtSNV number and MCN. For each of the nuclear features, we evaluated their correlation to 22 mitochondrial mutational features in 194 LOPCs with nuclear mutational data (Table 6). We detected multiple nuclear-mitochondrial mutational associations (
One prominent nuclear-mitochondrial mutational interactions was co-occurrence of MYC copy number gain and mtSNVs within the OHR (
Clinical Impact of mtSNVs in Prostate Cancer
The recurrence of mitochondrial mutations in specific regulatory regions and their association with prognostic nuclear mutations strongly suggested their ability to drive disease aggression. We therefore systematically evaluated the association of individual mitochondrial somatic mutational features with disease aggression in 165 patients with clinical follow-up using Cox proportional hazards modeling. Of our 22 mitochondrial mutational features (
These data suggested that mtSNVs might comprise a novel way to predict patient outcome. We therefore assessed the ability of a multi-mtSNV signature to identify patients at elevated risk for biochemical failure (who therefore might benefit from treatment intensification) and those at low risk (who might therefore be appropriate for surveillance protocols). Using leave-one-out cross-validation and univariate feature-selection, we created a three-class signature that separated patients into three distinct risk groups for biochemical failure (
The mitochondrial mutational landscape of cancer has been relatively unexplored. Previous work has shown a large-scale mtDNA deletion has predictive value in the prostate biopsy outcomes36, suggesting the feasibility of mtDNA-based molecular tests. We identify a large number of mtSNVs in localized prostate cancer. These mutations show complex interplay with nuclear mutational characteristics, and appear to work together to drive tumour aggressiveness.
Mitochondrial mutations also show associations with risk of biochemical relapse. Interestingly, mtSNVs within the control region can have conflicting outcomes, however when separated into the different noncoding subregions (HV1, OHR) we found that certain loci were associated with better outcomes and others with worse outcomes. The overlap of the OHR and HV2 within the control region and their association with MYC CNAs highlight the need for better understanding of the functions of the control region37. In future, treating the control region as distinct regulatory regions may provide further insight into the roles of these regions, as well as any contribution they may make towards tumour aggression. We note that the number of pairs of nuclear-mitochondrial mutational features tested may elevate false-positive rates, and it will be key to perform validation studies in larger cohorts to verify their effect-sizes and biological significance.
The differences observed in the mitochondrial mutational profiles of EOPC and LOPC patients show a need to better understand the association between mtSNVs and aging and how this may relate to the development of prostate cancer. While the mitochondrial copy number of matched-normal samples decreases with patient age, a previously observed trend33, tumour MCN estimates were significantly higher in older patients which could account for the higher frequency of mtSNVs in these patients. However, since the majority of the samples of each age group come from different research centres, this striking difference in tumour MCN will require further investigation to exclude any confounding effects.
Further studies will be needed to assess when different mtSNVs occur during tumour evolution, their timing relative to common nuclear mutations and the effects of these mutations on mitochondrial function. This will more clearly identify the mitochondrial mutations that are important for mitochondrial-nuclear communication and how they may interact to drive tumour formation. Localized prostate cancer remains the most diagnosed non-skin cancer in men, and identification of aggressive disease remains an urgent clinical dilemma. Addition of mtSNVs to prognostic biomarkers may be an effective way of improving prediction of patient outcome, supporting triage of patients with low-risk disease to surveillance protocols and with high-risk disease to adjuvant therapy regimens.
All documents disclosed herein, including those in the following reference list, are incorporated by reference. Although preferred embodiments of the invention have been described herein, it will be understood by those skilled in the art that that the detailed description and the specific examples while indicating preferred embodiments of the invention are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description.
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This application claims the benefit of priority of U.S. Provisional Patent Application No. 62/344,723 filed Jun. 2, 2016 and incorporated herein by reference in its entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/CA2017/000139 | 6/2/2017 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 62344723 | Jun 2016 | US |
