Cancer Origin Tracing and Timing in Two High-Risk Prostate Cancers Using Multisample Whole Genome Analysis: Prospects for Personalized Medicine

BACKGROUND: Prostate cancer (PrCa) genomic heterogeneity causes resistance to therapies such as androgen deprivation. Such heterogeneity can be deciphered in the context of evolutionary principles, but current clinical trials do not include evolution as an essential feature. Whether or not analysis of genomic data in an evolutionary context in primary prostate cancer can provide unique added value in the research and clinical domains remains an open question. METHODS: We used novel processing techniques to obtain whole genome data together with 3D anatomic and histomorphologic analysis in two men (GP5 and GP12) with high-risk PrCa undergoing radical prostatectomy. A total of 22 whole genome-sequenced sites (16 primary cancer foci and 6 lymph node metastatic) were analyzed using evolutionary reconstruction tools and spatio-evolutionary models. Probability models were used to trace spatial and chronological origins of the primary tumor and metastases, chart their genetic drivers, and distinguish metastatic and non-metastatic subclones. RESULTS: In patient GP5, CDK12 inactivation was among the first mutations, leading to a PrCa tandem duplicator phenotype and initiating the cancer around age 50, followed by rapid cancer evolution after age 57, and metastasis around age 59, 5 years prior to prostatectomy. In patient GP12, accelerated cancer progression was detected after age 54, and metastasis occurred around age 56, 3 years prior to prostatectomy. Multiple metastasis-originating events were identified in each patient and tracked anatomically. Metastasis from prostate to lymph nodes occurred strictly ipsilaterally in all 12 detected events. In this pilot, metastatic subclone content analysis appears to substantially enhance the identification of key drivers. Evolutionary analysis\u27 potential impact on therapy selection appears positive in these pilot cases. CONCLUSIONS: PrCa evolutionary analysis allows tracking of anatomic site of origin, timing of cancer origin and spread, and distinction of metastatic-capable from non-metastatic subclones. This enables better identification of actionable targets for therapy. If extended to larger cohorts, it appears likely that similar analyses could add substantial biological insight and clinically relevant value

PTPRD and CNTNAP2 as markers of tumor aggressiveness in oligodendrogliomas

Oligodendrogliomas are typically associated with the most favorable prognosis among diffuse gliomas. However, many of the tumors progress, eventually leading to patient death. To characterize the changes associated with oligodendroglioma recurrence and progression, we analyzed two recurrent oligodendroglioma tumors upon diagnosis and after tumor relapse based on whole-genome and RNA sequencing. Relapsed tumors were diagnosed as glioblastomas with an oligodendroglioma component before the World Health Organization classification update in 2016. Both patients died within 12 months after relapse. One patient carried an inactivating POLE mutation leading to a clearly hypermutated progressed tumor. Strikingly, both relapsed tumors carried focal chromosomal rearrangements in PTPRD and CNTNAP2 genes with associated decreased gene expression. TP53 mutation was also detected in both patients after tumor relapse. In The Cancer Genome Atlas (TCGA) diffuse glioma cohort, PTPRD and CNTNAP2 expression decreased by tumor grade in oligodendrogliomas and PTPRD expression also in IDH-mutant astrocytomas. Low expression of the genes was associated with poor overall survival. Our analysis provides information about aggressive oligodendrogliomas with worse prognosis and suggests that PTPRD and CNTNAP2 expression could represent an informative marker for their stratification.publishedVersionPeer reviewe

Genomic characterization of IDH-mutant astrocytoma progression to grade 4 in the treatment setting

As the progression of low-grade diffuse astrocytomas into grade 4 tumors significantly impacts patient prognosis, a better understanding of this process is of paramount importance for improved patient care. In this project, we analyzed matched IDH-mutant astrocytomas before and after progression to grade 4 from six patients (discovery cohort) with genome-wide sequencing, 21 additional patients with targeted sequencing, and 33 patients from Glioma Longitudinal AnalySiS cohort for validation. The Cancer Genome Atlas data from 595 diffuse gliomas provided supportive information. All patients in our discovery cohort received radiation, all but one underwent chemotherapy, and no patient received temozolomide (TMZ) before progression to grade 4 disease. One case in the discovery cohort exhibited a hypermutation signature associated with the inactivation of the MSH2 and DNMT3A genes. In other patients, the number of chromosomal rearrangements and deletions increased in grade 4 tumors. The cell cycle checkpoint gene CDKN2A, or less frequently RB1, was most commonly inactivated after receiving both chemo- and radiotherapy when compared to other treatment groups. Concomitant activating PDGFRA/MET alterations were detected in tumors that acquired a homozygous CDKN2A deletion. NRG3 gene was significantly downregulated and recurrently altered in progressed tumors. Its decreased expression was associated with poorer overall survival in both univariate and multivariate analysis. We also detected progression-related alterations in RAD51B and other DNA repair pathway genes associated with the promotion of error-prone DNA repair, potentially facilitating tumor progression. In our retrospective analysis of patient treatment and survival timelines (n = 75), the combination of postoperative radiation and chemotherapy (mainly TMZ) outperformed radiation, especially in the grade 3 tumor cohort, in which it was typically given after primary surgery. Our results provide further insight into the contribution of treatment and genetic alterations in cell cycle, growth factor signaling, and DNA repair-related genes to tumor evolution and progression.Peer reviewe

Tumor evolution and heterogeneity analysis of multiregional prostate cancer samples

Accumulation of mutations in the genomes of cells during lifetime can lead to uncontrollably dividing and misbehaving cell populations, that can ultimately cause the cancer onset. Investigating these changes in the genome has produced essential information for the treatment planning and for the development of new therapeutic methods in the battle against the cancer. However, more information can still be obtained from these genomic changes at individual levels. The development of personalized cancer medicine has become increasingly important part of cancer research due to the large variation between patients in the causes of disease onset and progression. It has been shown that the variation exists not only between patients, but also between the cells in the patients. Tumor heterogeneity has significant impact on the treatment efficiency, development of treatment resistance and disease relapse. Heterogeneous tumors are constructed of multiple different cancer cell populations, or subclones that contain distinct set of mutations. The subclonal composition can be reconstructed from the mutations by comparing their relative prevalence in sequencing data. Including multiple samples from single patient brings more information about the cell populations and allows more accurate and elaborate exploration of the subclonal architecture. The aim of this study was to set up a pipeline, which was used to perform evolution analysis on prostate tumor samples to find out the underlying subclonal composition in single primary tumor and its metastases. This thesis applies previously developed algorithms on two patient cases, each with 11 whole genome sequenced tumor samples, of which 3 were from metastases. This study setup is unique regarding the extent of sampling from primary prostate tumor in early disease state. Several subclones were detected representing the respective relations between different samples.Solujen genomeihin kerääntyy mutaatioita koko eliniän ajan. Jotkut näistä mutaatioista voivat johtaa hallitsemattomasti jakautuviin ja väärin käyttäytyviin solupopulaatioihin, jotka lopulta voivat aiheuttaa syövän puhkeamisen. Näiden genomissa tapahtuvien muutosten tutkiminen on tuottanut oleellista tietoa, jota hyödynnetään taistelussa syöpää vastaan hoidon valinnassa ja uusien hoitojen kehityksessä. Kuitenkin, yksilöllisellä tasolla voidaan vielä saada lisää tietoa näistä geneettisistä muutoksista. Yksilöllisten syöpähoitojen kehityksestä on tullut yhä tärkeämpi osa syöpätutkimusta, koska potilaiden välillä on havaittu paljon vaihtelua sairauden puhkeamisen ja etenemisen syissä. Vaihtelua ei ole vain potilaiden välillä, vaan myös potilaan solujen kesken. Kasvaimen heterogeenisyys vaikuttaa merkittävästi hoidon tehokkuuteen, hoidon resistanssin kehittymiseen ja syövän uusiutumiseen. Heterogeeniset kasvaimet koostuvat useista erilaisista solupopulaatioista, eli subklooneista, joista kukin sisältää erillisen joukon mutaatioita. Kasvaimen subklonaalinen koostumus voidaan rekonstruoida vertailemalla mutaatioiden suhteellista esiintyvyyttä sekvensointidatassa. Ottamalla mukaan useita syöpänäytteitä samasta potilaasta voidaan saada enemmän tietoa solupopulaatioista, mikä mahdollistaa täsmällisemmän ja yksityiskohtaisemman selvityksen subkloonirakenteesta. Tämän tutkimuksen tavoitteena oli luoda ympäristö, jossa evoluutioanalyysi suoritetaan usealle eturauhassyöpäkasvainnäytteelle, jotta saataisiin selville kasvaimen ja metastaasien perustana oleva subkloonien kokoelma. Työssä käytettiin aiemmin julkaistuja algoritmeja kahden potilastapauksen näytteisiin. Kummaltakin potilaalta sekvensoitiin koko genomit 11 kasvainnäytteestä, joista kolme oli peräisin etäpesäkkeistä. Primäärikasvaimesta otetut näytteet muodostavat ainutlaatuisen kattavan asetelman tutkittaessa eturauhassyöpää taudin varhaisessa vaiheessa. Tutkimuksessa löydettiin useita subklooneja, jotka edustavat eri näytteiden välisiä keskinäisiä yhteyksiä

PTPRD and CNTNAP2 as markers of tumor aggressiveness in oligodendrogliomas.

Oligodendrogliomas are typically associated with the most favorable prognosis among diffuse gliomas. However, many of the tumors progress, eventually leading to patient death. To characterize the changes associated with oligodendroglioma recurrence and progression, we analyzed two recurrent oligodendroglioma tumors upon diagnosis and after tumor relapse based on whole-genome and RNA sequencing. Relapsed tumors were diagnosed as glioblastomas with an oligodendroglioma component before the World Health Organization classification update in 2016. Both patients died within 12 months after relapse. One patient carried an inactivating POLE mutation leading to a clearly hypermutated progressed tumor. Strikingly, both relapsed tumors carried focal chromosomal rearrangements in PTPRD and CNTNAP2 genes with associated decreased gene expression. TP53 mutation was also detected in both patients after tumor relapse. In The Cancer Genome Atlas (TCGA) diffuse glioma cohort, PTPRD and CNTNAP2 expression decreased by tumor grade in oligodendrogliomas and PTPRD expression also in IDH-mutant astrocytomas. Low expression of the genes was associated with poor overall survival. Our analysis provides information about aggressive oligodendrogliomas with worse prognosis and suggests that PTPRD and CNTNAP2 expression could represent an informative marker for their stratification

Cancer origin tracing and timing in two high-risk prostate cancers using multisample whole genome analysis: prospects for personalized medicine.

BACKGROUND: Prostate cancer (PrCa) genomic heterogeneity causes resistance to therapies such as androgen deprivation. Such heterogeneity can be deciphered in the context of evolutionary principles, but current clinical trials do not include evolution as an essential feature. Whether or not analysis of genomic data in an evolutionary context in primary prostate cancer can provide unique added value in the research and clinical domains remains an open question. METHODS: We used novel processing techniques to obtain whole genome data together with 3D anatomic and histomorphologic analysis in two men (GP5 and GP12) with high-risk PrCa undergoing radical prostatectomy. A total of 22 whole genome-sequenced sites (16 primary cancer foci and 6 lymph node metastatic) were analyzed using evolutionary reconstruction tools and spatio-evolutionary models. Probability models were used to trace spatial and chronological origins of the primary tumor and metastases, chart their genetic drivers, and distinguish metastatic and non-metastatic subclones. RESULTS: In patient GP5, CDK12 inactivation was among the first mutations, leading to a PrCa tandem duplicator phenotype and initiating the cancer around age 50, followed by rapid cancer evolution after age 57, and metastasis around age 59, 5 years prior to prostatectomy. In patient GP12, accelerated cancer progression was detected after age 54, and metastasis occurred around age 56, 3 years prior to prostatectomy. Multiple metastasis-originating events were identified in each patient and tracked anatomically. Metastasis from prostate to lymph nodes occurred strictly ipsilaterally in all 12 detected events. In this pilot, metastatic subclone content analysis appears to substantially enhance the identification of key drivers. Evolutionary analysis' potential impact on therapy selection appears positive in these pilot cases. CONCLUSIONS: PrCa evolutionary analysis allows tracking of anatomic site of origin, timing of cancer origin and spread, and distinction of metastatic-capable from non-metastatic subclones. This enables better identification of actionable targets for therapy. If extended to larger cohorts, it appears likely that similar analyses could add substantial biological insight and clinically relevant value

Additional file 1 of Cancer origin tracing and timing in two high-risk prostate cancers using multisample whole genome analysis: prospects for personalized medicine

Additional file 1: Supplementary Material PDF including supplementary material index, supplementary tables 1 and 2, supplementary figures, supplementary methods, and supplementary references