A patient-driven clinicogenomic partnership for metastatic prostate cancer

Abstract

Molecular profiling studies have enabled discoveries for metastatic prostate cancer (MPC) but have predominantly occurred in academic medical institutions and involved non-representative patient populations. We established the Metastatic Prostate Cancer Project (MPCproject, mpcproject.org), a patient-partnered initiative to involve patients with MPC living anywhere in the US and Canada in molecular research. Here, we present results from our partnership with the first 706 MPCproject participants. While 41% of patient partners live in rural, physician-shortage, or medically underserved areas, the MPCproject has not yet achieved racial diversity, a disparity that demands new initiatives detailed herein. Among molecular data from 333 patient partners (572 samples), exome sequencing of 63 tumor and 19 cell-free DNA (cfDNA) samples recapitulated known findings in MPC, while inexpensive ultra-low-coverage sequencing of 318 cfDNA samples revealed clinically relevant AR amplifications. This study illustrates the power of a growing, longitudinal partnership with patients to generate a more representative understanding of MPC.

Graphical abstract

Figure 1

Partnering with diverse patients to enhance our understanding of metastatic prostate cancer (A) Summary of MPCproject enrollment process. Patients learn about the project primarily through outreach and partnered advocacy groups. If they register, patient partners complete online intake, consent, and medical release forms, then can opt into donating saliva via a mailed kit and/or blood at routine blood draws at no charge. In parallel, MPCproject staff request medical records and archival tumor samples from patients’ medical institutions, then abstract medical information from obtained records and sequence archival tumor tissue and/or donated blood and saliva (STAR Methods). Deidentified clinical, genomic, and patient-reported data are released on a continual, prepublication basis and deposited in public repositories. (B) Enrollment statistics and timeline for the MPCproject. Depicted are the cumulative number of patients that began the registration process (registered), patients that completed the survey and consent forms (enrolled), patients with at least one medical record received (medical records), and blood kits, saliva kits, and archival tumor tissue received at the Broad Institute for sequencing (blood kits, saliva kits, and tumor tissue, respectively). 706 patient partners enrolled before “study cutoff,” June 1, 2020, and are included in this study’s analyses. cBioPortal (cbioportal.org) releases include summary abstracted medical, genomic, and patient-reported data; Genomic Data Commons (GDC) releases include raw sequencing files and demographic data. (C) Represented medical institutions among patient partners living in the US and Canada. Shown are the 1,049 unique institutions (x axis) where patient partners report receiving care for their prostate cancer, with the number of distinct patient partners at each institution (y axis). NCI-designated cancer centers are shown in green. Patient partners that did not complete this survey question (n = 36) and institutions outside the US and Canada (n = 56) are not shown. (D) Access to medical care among patient partners living in the US. Patient-reported data were used to identify residential census tracts that were overlapped with primary care health-physician-shortage areas (HPSAs), medically underserved population/areas (MUAs), and rural areas obtained from the Health Resources and Services Administration and US Census. Patient partners that live in Canada (n = 30) who did not provide residential data (n = 40) or who provided only a P.O. box (n = 8) are not shown. (E) Patient partners living in more disadvantaged areas are less likely to attend NCI cancer centers. The Area Deprivation Index, a metric that assesses neighborhood disadvantage, was assessed for each residential census block group. Higher values indicate more disadvantage. The x axis reflects whether patient partners reported receiving care at an NCI-designated cancer center. ∗∗∗ p < 0.001 in a logistic regression model that adjusts for rural, MUA, and HPSA status.

Figure 2

Patient voices reveal the landscape of living with metastatic prostate cancer (A–D) Self-reported data of 706 patient partners related to their prostate cancer. (A) Patient partners were asked for the current location of their cancer. Participants were free to choose multiple if their cancer had metastasized to multiple locations. (B–D) Responses were tabulated from questions asking patient partners if their initial prostate cancer diagnosis was metastatic (B), if they have a family history of prostate/breast cancer (C), or if they have ever had another cancer diagnosis (D). Patient partners who did not complete these questions (n < 5 for all questions) are not shown. (E) Self-reported therapies show strong overlap with medical records. Therapy categories are shown on the y axis, with the proportion of patient partners from each data type (patient surveys and medical records) receiving therapies of that category shown on the x axis. In the online survey, patient partners selected therapies they received for their metastatic prostate cancer from a list. 639/706 patient partners reported at least one therapy and are shown. 119 of these participants also had abstracted therapy data from medical records. Report overlap refers to how often patient partners report receiving a therapy when their medical records show that they have received that therapy as a percentage. Only therapies available for selection in the patient survey were used in this comparison (Table S4). (F) Landscape of lifestyle changes for patient partners. Participants were asked to list additional medications, alternative medications, or lifestyle changes since their diagnosis of prostate cancer. Free-text responses were manually abstracted and categorized into diet/lifestyle changes, supplements, and non-cancer medications. The y axis shows individual instances of diet/lifestyle changes, supplements, or medications. The x axis shows the percentage of patient partners with that lifestyle change or that were taking that supplement/therapy out of all patient partners that responded to the lifestyle question (n = 456). CBD/THC, cannabidiol/tetrahydrocannabinol (oils, medical marijuana, etc.).

Figure 3

Remotely donated tumor and cell-free DNA samples obtained through patient partnership recapitulate known genomic findings in metastatic prostate cancer (A) Genomic and clinical landscape of 82 sequenced samples. Columns represent samples, separated into tumor (prostate, left) and cell-free DNA (cfDNA; donated blood, right) samples, while rows represent select clinical and genomic features. Gleason scores for tumor samples are taken from the pathology report received with the sample (n = 58) or the patient partner’s medical records (n = 5) if Gleason scores were not provided in the report. Gleason scores for cfDNA were taken from pathology reports in the medical record, with NR representing cases where a Gleason score was not reported in the medical record. Diagnosis refers to whether the initial diagnosis of prostate cancer was localized or metastatic. Multiple mutations in the same gene are represented as triangles. WGD refers to whole-genome doubling. Copy-number calls are allelic and defined with respect to baseline allelic ploidy (2 for samples with WGD, one for those without), with calls for the two alleles indicated by two triangles (except for AR, which has only one allele in men and so is shown as a single box). Allelic CN = 0 refers to complete allelic deletions. Allelic deletions that are not complete deletions are possible in samples with WGD. Figure created with CoMut. (B) Mutational signature analysis of sequenced samples. The relative contribution of select COSMIC v.2.0 mutational signatures are shown, separated by tumor and cfDNA (donated blood) sample type. APOBEC refers to signatures associated with activity of APOBEC family of cytidine deaminases (signatures 2 and 13); MMR to the signature associated with deficient DNA mismatch repair (signature 6); and HRD to the signature associated with homologous recombination deficiency (signature 3). To be denoted as present, a signature cutoff of 6% was used. Samples with too few mutations for signature analysis (<50 mutations, n = 5 samples) are not shown. (C) Instance of localized hypermutation (kataegis) of KMT2C in cfDNA from a donated blood sample. The y axis shows the cancer cell fraction of each mutation, while the x axis shows their amino acid within KMT2C. Domains taken from Pfam. The dotted line connects to this sample’s mutational signature profile. (D and E) Germline pathogenic alterations and their overlap with patient-reported family history. Pathogenic germline alterations (as annotated by ClinVar) in genes from a select panel of genes previously implicated in cancer heritability were detected in patient partners with sequenced saliva or blood buffy coat (n = 132) (STAR Methods; Tables S3 and S5). Survey responses to a question asking about a family history of prostate or breast cancer were tabulated and overlapped with this genomic data. Stars in (E) indicate instances where a somatic deletion also affected that gene in a tumor or cfDNA sample from that patient partner, suggesting biallelic inactivation.

Figure 4

cfDNA from donated blood reveals patterns of clonal dynamics and clinically relevant genomic changes (A) Clinical trajectory of patient partner 0495. This patient partner’s prostate-specific antigen (PSA) trajectory is shown on the y axis, time in years since initial diagnosis is shown on the x axis, and bars denote the beginning and end of therapies. EBRT, external beam radiation therapy; first-line androgen deprivation therapy (ADT), leuprolide and bicalutamide; immunotherapy, nivolumab; chemotherapy, cisplatin and etoposide. (B) Tumor evolution from primary tumor to metastatic cfDNA samples. The y axis shows the cancer cell fraction (CCF) of clonal clusters identified between tumor and cfDNA samples (x axis). Time between samples shown on the x axis. Colors indicate how many mutations were identified in each clone, with a 95% confidence interval around the estimated CCF. Purple represents the truncal/ancestral clone. Clusters with CCF <0.10 across all biopsies are omitted. The clinical trajectory of patient partner 0495 (left) is shown in (A), while the trajectory of patient partner 0093 (right) is shown in (C). (C) Emergence of AR amplification in patient partner 0093 induced by anti-androgen therapy. The timeline depicts this patient’s clinical trajectory, while the plots show the absolute copy number (y axis) of the genomic region around AR (x axis, gene body shown in gray). The first plot depicts exome sequencing from the patient’s archival tumor tissue; the second and third plots depict ultra-low-pass whole-genome sequencing (ULP-WGS) and exome sequencing of cfDNA from the patient’s donated blood, respectively. Individual points represent copy number of target regions (exome) or copy number of 1 Mb genomic windows (ULP-WGS). Black lines represent discrete copy-number segments. (D–F) ULP-WGS reveals clinically relevant AR amplifications even at low tumor fraction. In (D), tumor fraction of 318 cfDNA samples from donated blood of 300 patient partners with ULP-WGS sequencing is shown on the x axis, while the log copy ratio (logR) of the genomic interval containing AR is shown on the y axis. Points are colored by whether patient partners self-reported taking enzalutamide or abiraterone. 89 samples are shown with tumor fraction of 0 (undetectable), while 229 have non-zero tumor fractions. Two samples, one at a tumor fraction of 0 and another at a tumor fraction of 0.023, have chromosome X log copy ratio profiles shown in (E) and (F), respectively. The green points represent the values shown in (D), with the genomic interval containing AR highlighted in gray.