Cancer cells carry different types of mutations that are associated with the cell starting to multiply uncontrollably. Certain changes only affect one or a few letters of the genetic code. Others, known as copy number alterations, or CNA, involve larger portions of the genome that can either be lost (deletions) or duplicated (amplifications). Tumors in different patients carry variable amounts of these deletions or amplifications, which together are known as the CNA burden.

New technologies allow scientists to scan the genomes of tumors and examine the type of mutations present in each patient. The results can help to decide on the best course of action. For example, in prostate cancer, patients whose tumors have a high CNA burden are at greater risk of relapse after treatment. However, it has been unclear whether these people also have lower survival rates, and if CNA burden can predict outcome of other types of cancers.

Hieronymus et al. conducted genetic analyses on over a hundred samples from prostate cancer patients who were not treated with surgery or radiation. The results showed that a higher CNA burden in the tumors is correlated with more deaths due to the disease. The findings in prostate cancer were also true across different types of cancers. These conclusions also emerged when Hieronymus et al. then looked at genomic data obtained from patients with various cancers using a different DNA sequencing test, which is certified for clinical use. This demonstrates that CNA burden could be a useful marker in clinical settings to help assess risk in cancer patients.

Somatic copy number alterations (CNAs) are nearly ubiquitous in cancer (Zack et al., 2013; Heitzer et al., 2016) and alter a greater portion of the cancer genome than any other type of somatic genetic alteration (Heitzer et al., 2016). Different cancer types vary in their balance of copy number alterations to somatic point mutations, with prostate cancer having relatively high rates of CNA compared to point mutation. Given the prevalence of CNAs in cancer, significant effort has been directed towards identifying specific CNAs associated with cancer clinical characteristics and prognosis as well as the potential driver genes they contain (Liang et al., 2016; Wang et al., 2016; Nibourel et al., 2017). There are well demonstrated associations between specific CNAs and CNA signatures to cancer state and characteristics (Visakorpi et al., 1995; Williams et al., 2014; Taylor et al., 2010). CNV patterns or clusters have been associated with high Gleason prostate cancer (Gleason 8 + compared to Gleason 6–7 [Williams et al., 2014]) and recurrent disease (compared to primary [Visakorpi et al., 1995; Cancer Genome Atlas Research Network, 2015; Viswanathan et al., 2018]). Nonetheless, most CNAs are large, (Zack et al., 2013; Beroukhim et al., 2010) and their associations with cancer outcome may not be well identified by gene-specific approaches. Increasing evidence indicates that large CNAs harbor multiple drivers (Tschaharganeh et al., 2016; Liu et al., 2016), emphasizing the need to study their biological and clinical significance beyond individual gene-focused standpoints.

The CNA burden of a tumor is the degree to which a tumor's genome is altered as a percentage of genome length and represents a fundamental measure of genome copy number alteration level. As such, tumor CNA burden, rather than individual CNAs, may be associated with cancer outcomes. While tumor mutational burden (TMB) predicts response to immunotherapy across multiple cancer types (Bergerot et al., 2018; Goodman et al., 2017), tumor CNA burden may be prognostic for outcomes such as recurrence and survival. Indeed, we and others have previously found CNA burden and genome-wide CNA patterns to be associated with biochemical recurrence and metastasis in primary prostate cancer, the most common cancer in men, across multiple cohorts (Taylor et al., 2010; Hieronymus et al., 2014; Camacho et al., 2017). This prognostic significance of tumor CNA burden extends to low and intermediate risk prostate cancer (Gleason scores of 7 and less) (Hieronymus et al., 2014) and has the potential to better stratify risk in patients who are considering conservative treatment approaches such as active surveillance to reduce overtreatment (Chen et al., 2016; Tosoian et al., 2016).

In addition to questions about the prognostic potential and overall landscape of CNA in conservatively treated prostate cancer, it is unknown whether CNA burden is prognostic for prostate cancer survival, rather than only recurrence and metastasis. Nor is it known whether the prognostic significance of tumor CNA burden extends to other cancer types. Here we set out to address these questions, as well as whether tumor CNA burden can be prognostic in a clinical practice setting, including (i) in cancers treated conservatively rather than through immediate surgery or radiation, (ii) in biopsy or resection samples, and (iii) using a clinical targeted sequencing that allows rapid and cost-effective measurement of tumor CNA burden.

To address these questions, we first examine the genomic CNA landscape of conservatively treated prostate cancer in more than a hundred diagnostic biopsy and resection specimens from a conservatively treated cohort; this cohort consisted of patients with localized prostate who were not treated with surgery or radiation within six months of diagnosis. We demonstrate that tumor CNA burden is associated with cancer-specific death, independent of standard clinical predictors. To explore the prognostic significance of tumor CNA burden more broadly in other cancer types, we find that tumor CNA burden is also associated with disease-free and overall survival in TCGA cohorts of primary breast, endometrial, renal clear cell, thyroid, and colorectal cancer in addition to prostate cancer, with the degree of association varying in some cancer types. We then establish the clinical feasibility of measuring tumor CNA burden using the FDA-cleared MSK-IMPACT clinical next generation sequencing (NGS) assay and confirm that tumor CNA burden is associated with overall and disease-specific survival in both primary and metastatic tumors across cancer types. In all, we demonstrate that tumor CNA burden is a prognostic factor associated with cancer recurrence and death in multiple cancer types, including in conservatively treated prostate cancer which would benefit from increased risk stratification.

Many specific genes altered by CNA have been associated with cancer outcomes (Liang et al., 2016; Wang et al., 2016; Nibourel et al., 2017), however the relationship between outcome and the overall level of CNA harbored by a tumor is less well studied. Here we expanded on our previous work showing that tumor CNA burden is associated with recurrence in surgically treated primary prostate cancer (Taylor et al., 2010; Hieronymus et al., 2014) by showing a significant association with death from prostate cancer, including in conservatively treated patients where the tumor CNA burden measurement was made from biopsies. Importantly, this association remains significant even after adjusting for Gleason score or for CAPRA score, demonstrating that CNA burden is independent of previously identified associations with these measures of cancer pathology or disease state. Thus, tumor CNA burden assessment from prostate biopsies could have a role in deciding between surgery and surveillance for men at the low end of intermediate risk. Conversely, it may also have role in men at high risk where multimodal treatment may be needed.

An unanticipated outcome of our analysis beyond prostate cancer is the prognostic role of tumor CNA burden across a range of tumor types. The pan-cancer tumor CNA burden association is significant but also heterogeneous depending on cancer type. Recent work has similarly found that the presence of any CNA, regardless of gene identity, is associated with overall and event-free survival in pediatric AML (Vujkovic et al., 2017) and that the percentage of subclonal CNAs but not subclonal somatic point mutations is associated with overall survival in non-small cell lung cancer (Jamal-Hanjani et al., 2017). Moreover, survival time was associated with a CNA signature derived from supervised analyses in prostate cancer and extended to breast and lung cancer (Pearlman et al., 2018). Prognostic individual CNAs or sets of CNAs, as opposed to the broader measure of genome-wide CNA level examined here may be specific to individual cancer types, whereas we have demonstrated the prognostic potential of a generalized measure of overall copy number dysregulation. Further work will be needed to address the trade-offs between generalizability of CNA burden and discriminatory power. In addition, it will be important to investigate whether the prognostic associations of CNA burden from the pan-cancer analysis are independent of known cancer- or subtype-specific prognostic factors, such as ER receptor status in breast cancer, ultra- and hypermutated (POLE and MSI+) status in endometrial cancer and MSI-positive/CIN-negative status in colorectal cancer (Walther et al., 2008).

We find it notable that tumor CNA burden assessment using a targeted sequencing can serve as a surrogate for tumor CNA burden calculated using more comprehensive genomic assays such as array CGH. With the proliferation of different clinical sequencing panels for mutation detection, it will be of interest to see how much resolution, depth, and coverage can be reduced and still retain the prognostic association of CNA burden; future work in this area will also need to incorporate the predictive clinical utility of the point mutation data to address the multimodal uses of clinical sequencing assays. Another important variable is tumor purity. The prognostic significance of CNA burden can be affected by sample tumor purity, with purity being independently associated with outcome. The effect of purity on the association between CNA burden and outcome appears complex and may be influenced by the analysis platform, cancer type, and outcome type. For example, pan-cancer CNA burden from clinical sequencing panel remained prognostic for survival after purity adjustment in primary tumors and was just below significance for metastatic tumors, though the CNA burden of the prostate tumor subset assayed by IMPACT sequencing panel did not. However, the CNA burden of prostate tumors assayed by SNP array showed continued association with recurrence after adjustment for purity. Tumor purity alone was also independently associated with survival, revealing a complex interaction between these tumor features that will need further exploration. As targeted sequencing moves from tumor samples to liquid biopsy in the form of cell-free DNA (cfDNA) (Heitzer et al., 2016; Xia et al., 2015; Hyman et al., 2017), it will be important to determine whether tumor CNA burden determined by analysis of cfDNA has similar prognostic utility as that determined by direct analysis of tumor DNA. There is already some evidence this may be possible, as the summed CNA level of the most highly copy number altered genes assayed from whole genome sequencing of cfDNA in twenty metastatic prostate cancer patients correlated with overall survival (Xia et al., 2015). As sequencing costs continue to drop and computational power improves, it would be interesting to investigate low pass whole genome sequencing as an alternative approach for determining tumor CNA burden that provides complete genome coverage.

Another interesting feature of the association of tumor CNA burden with outcome demonstrated here is that it has prognostic significance independent of tumor mutation burden (TMB). This is consistent with recent work in glioblastoma, breast, lung, and ovarian cancer showing that CNA-derived signatures have more prognostic power than somatic point mutation-based signatures, as measured by concordance index (Gómez-Rueda et al., 2015). Thus, tumor CNA burden could complement clinical analyses of actionable driver mutations using a single panel-based sequencing assay.

The prognostic significance of tumor CNA burden raises intriguing questions regarding the underlying biology. Tumor CNA burden may be a simple measure that correlates with the extent of oncogenic driver alterations. Yet, we show that tumor CNA burden retains its prognostic significance after adjustment for a number of known oncogenic alterations in primary prostate cancer, including PTEN loss associated with increased tumor CNA burden (Castro et al., 2015; Williams et al., 2014). In metastatic tumors, combining tumor CNA burden with TP53 or RB1 loss in multivariable analyses renders both slightly below conventional significance thresholds, raising the possibility of biological interplay between these genes (particularly TP53) and subsequent copy number alteration that develops during tumor evolution. Further, the prognostic associations of tumor CNA burden are independent of tumor ploidy, which suggests that tumor CNA burden may not simply reflect aneuploidy, defined as abnormal DNA content (Danielsen et al., 2016). It is also possible that tumor CNA burden captures prognostic information about currently unidentified driver alterations and/or the rate of ongoing CNA within a tumor that may generate additional driver alterations, including those reflecting intratumoral heterogeneity, thereby affecting outcome. Ongoing work by others has begun to develop genomic methods for identifying mechanisms of somatic CNA (Wala et al., 2017); and identify prognostic CNA signatures and the mechanisms underlying the component CNA (Macintyre et al., 2018). Ultimately, the biology underlying the significant association of tumor CNA burden with multiple cancer outcomes will be a fruitful area for future investigation.

All data generated or analysed during this study are included in the manuscript and supporting files and reference materials. The conservative treatment TAPG copy number cohort array data was deposited in NCBI GEO under accession number GSE103665 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE103665).

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Author details

1. Haley Hieronymus

   Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

2. Rajmohan Murali

   Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, United States

   Contribution

   Data curation, Methodology, Project administration

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6988-4295

3. Amy Tin

   Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, United States

   Contribution

   Data curation, Formal analysis, Visualization, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

4. Kamlesh Yadav

   Department of Urology, Icahn School of Medicine at Mount Sinai, New York, United States

   Contribution

   Investigation, Project administration

   Competing interests

   No competing interests declared

5. Wassim Abida

   1. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, United States
   2. Genitourinary Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, United States

   Contribution

   Data curation

   Competing interests

   No competing interests declared

6. Henrik Moller

   Department of Cancer Epidemiology, Population and Global Health, King's College London, London, United Kingdom

   Contribution

   Resources, Data curation, Methodology

   Competing interests

   No competing interests declared

7. Daniel Berney

   Department of Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom

   Contribution

   Resources, Data curation

   Competing interests

   No competing interests declared

8. Howard Scher

   1. Genitourinary Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, United States
   2. Department of Medicine, Weill Cornell Medical College, New York, United States

   Contribution

   Conceptualization, Resources, Supervision

   Competing interests

   No competing interests declared

9. Brett Carver

   Department of Urology, Memorial Sloan Kettering Cancer Center, New York, United States

   Contribution

   Conceptualization, Writing—review and editing

   Competing interests

   No competing interests declared

10. Peter Scardino

    Department of Urology, Memorial Sloan Kettering Cancer Center, New York, United States

    Contribution

    Conceptualization, Funding acquisition

    Competing interests

    No competing interests declared

11. Nikolaus Schultz

    Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, United States

    Contribution

    Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

12. Barry Taylor

    1. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, United States
    2. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, United States
    3. Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, United States

    Contribution

    Conceptualization, Investigation, Visualization, Writing—review and editing

    Competing interests

    No competing interests declared

13. Andrew Vickers

    Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, United States

    Contribution

    Conceptualization, Formal analysis, Writing—review and editing

    Competing interests

    No competing interests declared

14. Jack Cuzick

    Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, United Kingdom

    Contribution

    Data curation, Validation, Investigation, Project administration

    Competing interests

    No competing interests declared

15. Charles L Sawyers

    1. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, United States
    2. Howard Hughes Medical Institute, Chevy Chase, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Writing—review and editing

    For correspondence

    sawyersc@mskcc.org

    Competing interests

    Senior Editor, eLife; Board of Directors of Novartis; co-founder of ORIC Pharm; co-inventor of enzalutamide and apalutamide; Science advisor to Agios, Beigene, Blueprint, Column Group, Foghorn, Housey Pharma, Nextech, KSQ, Petra and PMV; co-founder of Seragon, purchased by Genentech/Roche in 2014

    "This ORCID iD identifies the author of this article:" 0000-0003-4955-6475

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