Risk Modeling: Predicting cancer risk based on family history

A new software package provides more accurate cancer risk prediction profiles and has the ability to integrate more genes and cancer types in the future.
  1. Michelle F Jacobs  Is a corresponding author
  1. Internal Medicine, University of Michigan, United States

Countless hours have been dedicated to researching cancer – how to prevent it, how to diagnose it early, and how to treat it. Yet, cancer remains a leading cause of death worldwide, accounting for almost 10 million fatalities in 2020.

Most cancers are caused by changes to genes that happen over a person’s lifetime. In rarer cases (about 5–10%), they start due to inherited genetic mutations that produce a predisposition to cancer. In these instances, also known as familial or hereditary cancer syndromes, the mutation is passed down from generation to generation. In these families, more members tend to develop cancers than expected – often of the same or related type – which can also start at a particularly early age.

It is important to identify people with such genetic mutations so that they – and any family members at higher risk – can undergo enhanced cancer screening. Family history can be a useful predictor of hereditary cancer risk (Blackford and Parmigiani, 2010). As such, risk prediction models that incorporate family history to estimate a person’s chance of having a mutation in a cancer predisposition gene or of developing cancer have been employed for many years (Chen et al., 2004).

Historically, such models have been particularly valuable for deciding who to offer genetic testing to when only few and often costly genetic tests were available (Fasching et al., 2007). In some cases, insurance companies require the risk estimate related to carrying a cancer-related genetic mutation to exceed a certain threshold (typically 5 or 10%) to reimburse the cost of a genetic test (Chen et al., 2006). As research advances, the number of genes available for cancer-related genetic testing has now reached over 100 and is likely to continue increasing. Nevertheless, older risk modeling programs generally include only a small number of genes in their predictions. Now, in eLife, Danielle Braun and colleagues – including Gavin Lee and Jane Liang as joint first authors – report on a new software package that has the capacity to evolve alongside advances in cancer research (Lee et al., 2021).

The researchers, who are based at ETH Zürich, EPFL, Harvard, the Dana-Farber Cancer Institute, and the Broad Institute, developed PanelPRO, a tool that uses evidence gathered from extensive literature reviews to model the complex interplay between genes and cancer risk. PanelPRO’s workflow consists of four main parts: input, preprocessing, algorithm, and output (Figure 1).

Workflow for PanelPRO.

First, information on family history, including cancer diagnoses, age of relatives, and cancer risk factors is added into the risk modeling software PanelPRO (input, blue box on the left). Then, PanelPRO validates data formatting (preprocessing, grey oval), and analyses information about frequency and cancer risks for family cancer syndromes (algorithm, grey box) to estimate the likelihood of a person in a family having a mutation in a gene linked to an increased risk of cancer (output, green box on the right). Mutation probability and cumulative cancer risk are given as a probability between 0.0 (no risk) and 1.0 (100% risk).

The user first adds information about a history of cancers in a family – such as ages and cancer diagnoses – and other factors that might affect cancer risk. These include any risk-reducing surgeries in relatives, or tumors with biomarkers that might indicate a potential hereditary cause of their cancer. The software then adds information on the frequency of different hereditary cancer syndromes and assesses their associated cancer risks. PanelPRO can currently accommodate 18 types of cancer and generate predictions of probable mutations for 24 genes, but its code allows for the addition of new cancers or cancer-related genes that may be identified in the future.

During the preprocessing stage, the software verifies the input for any missing information and data, and also for any family relationships not supported by the software, such as ‘double cousins’, which occur when two siblings have children with two siblings from another family. Messages, warnings, or errors may be given to the user if any issues are detected.

After the information has been checked and modified as needed, the model proceeds to the algorithm stage. To calculate the output, the algorithm uses probabilities based on the family history, the frequency of hereditary cancer syndromes in the population, and the cancer history that would be expected if a cancer syndrome were present. The program then estimates the likelihood of a person in a family to have a mutation in a gene linked to an increased risk of cancer. These calculations can also be easily run for other family members using the existing information. It also shows a personalized estimate of future cancer risks. Users can choose which cancer types and genes to display.

However, some outstanding issues remain. Misreported family history information, such as an inaccurate cancer diagnosis or unknown age of diagnosis, can significantly affect estimates, highlighting that accuracy of patient-reported information is key to producing correct estimates (Katki, 2006). While patients have been shown to generally provide exact information on cancer history for first-degree relatives, the accuracy of these reports decreases for more distant relations (Augustinsson et al., 2018; Murff et al., 2004).

Moreover, analyses with a similar risk modeling software have revealed that a strict adherence to a 10% risk threshold to qualify for a test for a probable mutation in the BRCA gene (which is linked to an increased risk of developing breast, ovarian, and other cancers) would miss around 25% of individuals carrying a mutation when compared to genetic testing outcomes (Varesco et al., 2013). This is likely because cancer risks associated with hereditary cancer syndromes are more variable than initially appreciated, and not all family histories may exhibit a predictable pattern of cancer, even when a mutation is present (Okur and Chung, 2017). This complicates risk assessments and argues against making decisions about genetic testing solely based on risk prediction models. Today, broader insurance coverage guidelines and lower costs for genetic tests have increased clinicians’ ability to order these tests, even if certain risk thresholds are not met based on family history.

Nevertheless, the higher number of genes and cancer types supported by PanelPRO compared to other risk models are impressive and its ability to incorporate new genes and cancer types as testing advances are key in this fast-paced, constantly advancing field.

References

  1. Book
    1. Blackford A
    2. Parmigiani G
    (2010) Familial cancer risk assessment using BayesMendel
    In: Michael F. O, John T. C, editors. Biomedical Informatics for Cancer Research. Springer. pp. 301–314.
    https://doi.org/10.1007/978-1-4419-5714-6

Article and author information

Author details

  1. Michelle F Jacobs

    Michelle F Jacobs is in the Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, United States

    For correspondence
    mfjac@med.umich.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0458-1952

Publication history

  1. Version of Record published: September 29, 2021 (version 1)

Copyright

© 2021, Jacobs

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 588
    Page views
  • 33
    Downloads
  • 1
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Michelle F Jacobs
(2021)
Risk Modeling: Predicting cancer risk based on family history
eLife 10:e73380.
https://doi.org/10.7554/eLife.73380

Further reading

    1. Cancer Biology
    Laura M Sipe et al.
    Research Article

    Bariatric surgery is becoming more prevalent as a sustainable weight loss approach, with vertical sleeve gastrectomy (VSG) being the first line of surgical intervention. We and others have shown that obesity exacerbates tumor growth while diet-induced weight loss impairs obesity-driven progression. It remains unknown how bariatric surgery-induced weight loss impacts cancer progression or alters responses to therapy. Using a pre-clinical model of diet induced obesity followed by VSG or diet-induced weight loss, breast cancer progression and immune checkpoint blockade therapy was investigated. Weight loss by bariatric surgery or weight matched dietary intervention before tumor engraftment protected against obesity-exacerbated tumor progression. However, VSG was not as effective as dietary intervention in reducing tumor burden despite achieving a similar extent of weight and adiposity loss. Circulating leptin did not associate with changes in tumor burden, however circulating IL-6 was elevated in mice after VSG. Uniquely, tumors in mice that received VSG displayed elevated inflammation and immune checkpoint ligand PD-L1+ myeloid and non-immune cells. Further, mice that received VSG had reduced tumor T lymphocytes and markers of cytolysis suggesting an ineffective anti-tumor microenvironment. VSG-associated elevation of PD-L1 prompted us to next investigate the efficacy of immune checkpoint blockade in lean, obese, and formerly obese mice that lost weight by VSG or weight matched controls. While obese mice were resistant to immune checkpoint blockade, anti-PD-L1 potently impaired tumor progression after VSG through improved anti-tumor immunity. Thus, in formerly obese mice, surgical weight loss followed by immunotherapy reduced breast cancer burden. Last, we compared transcriptomic changes in adipose tissue after bariatric surgery from both patients and mouse models that revealed a conserved bariatric surgery associated weight loss signature (BSAS). Importantly, BSAS significantly associated with decreased tumor volume. Our findings demonstrate conserved impacts of obesity and bariatric surgery-induced weight loss pathways associated with breast cancer progression.

    1. Cancer Biology
    2. Cell Biology
    Johnny M Tkach et al.
    Research Article

    Centrosomes act as the main microtubule organizing center (MTOC) in metazoans. Centrosome number is tightly regulated by limiting centriole duplication to a single round per cell cycle. This control is achieved by multiple mechanisms, including the regulation of the protein kinase PLK4, the most upstream facilitator of centriole duplication. Altered centrosome numbers in mouse and human cells cause p53-dependent growth arrest through poorly defined mechanisms. Recent work has shown that the E3 ligase TRIM37 is required for cell cycle arrest in acentrosomal cells. To gain additional insights into this process, we undertook a series of genome-wide CRISPR/Cas9 screens to identify factors important for growth arrest triggered by treatment with centrinone B, a selective PLK4 inhibitor. We found that TRIM37 is a key mediator of growth arrest after partial or full PLK4 inhibition. Interestingly, PLK4 cellular mobility decreased in a dose-dependent manner after centrinone B treatment. In contrast to recent work, we found that growth arrest after PLK4 inhibition correlated better with PLK4 activity than with mitotic length or centrosome number. These data provide insights into the global response to changes in centrosome number and PLK4 activity and extend the role for TRIM37 in regulating the abundance, localization, and function of centrosome proteins.