Genetic mutations that increase cancer risk can be passed down from parents to their children, which can affect families across many generations. In these families, multiple members may be affected by different types of cancer, and these cancers often develop at an early age. Unaffected family members are often referred to genetic counselling, where they can explore their own risk of cancer. Clinicians and genetic counselors can provide recommendations to minimize cancer risk and inform personal choices on how to manage that risk, such as opting for preventative surgeries or participating in regular screening.

In genetic counselling sessions, highly trained clinicians and specialists use software that takes an individual’s family history of cancer and uses it to estimate their individual risk of carrying certain genetic mutations. These estimates can in turn help to predict their future risk of cancer. Many existing software packages are limited to estimating risks based on mutations in well-known cancer-related genes, such as BRCA1 and BRCA2 in breast and ovarian cancer. However, emerging evidence suggests that many of the genes associated with cancer risk work as part of a complex and overlapping network. Since current risk-profiling software packages are only designed to consider such genes in isolation, they cannot generate the most robust, accurate or comprehensive cancer risk profiles.

To address this challenge, Lee, Liang et al. have developed a new risk-profiling software that can integrate a large number of gene mutations and a wide range of potential cancer types to provide more accurate estimates of individual cancer risk. This software, called PanelPRO, uses evidence identified from extensive literature reviews to model the complex interplay between genes and cancer risk. The software not only calculates risks based on known genes, but also allows other developers to integrate new cancer-related genes that may be identified in the future. Importantly, the software is compatible with genetic counselling applications, since it returns answers within seconds when reasonable family and gene database sizes are used.

PanelPRO is a new, modern, flexible and efficient software package that provides an important advance towards modelling the vast genetic and biological complexity that contributes to inherited cancer risk. This software is designed to provide a more accurate and comprehensive estimate of cancer risk for individuals with family histories of cancer.

As an open-source software, it is freely available for research purposes, and can be licensed by software companies and healthcare organizations to integrate electronic patient records and rapidly identify at-risk individuals across larger patient groups. Ultimately, this software has the potential to improve cancer prevention strategies and optimize the personalized decision-making processes around cancer risk.

In the last decade, DNA sequencing has changed dramatically. Tests have become faster and more affordable, leading to discovery of a growing number of germline pathogenic variants associated with increased cancer risk. Multi-gene panels are routinely available and include varying combinations of genes (Plichta et al., 2016). Evidence is accruing that gene mutations, which were typically believed to be only associated with one or two types of hereditary cancers, may in fact increase the risk for a wider range of syndromes. These advancements have changed the genetic counseling landscape by introducing a need to consider a wider set of individual genes and cancers to accurately assess overall risks. In the context of genetic counseling, the importance of accurate estimates of carrier probabilities is well-known (Nelson et al., 2014). With the number of genes of interest and their combinations increasing, efficient calculation of these estimates becomes crucial in clinical settings.

In genetic counseling, an individual may be suspected of inherited cancer susceptibility if their family history exhibits certain patterns. For example, if two or more relatives have the same type of cancer on the same side of the family, or if cancer diagnoses in the family are particularly early, they may be referred to testing for mutations in genes associated with increased risk for those specific cancers. In the case of hereditary breast cancer, guidelines in the US were established to identify patients who have higher likelihoods of benefiting from germline genetic testing. Thresholds for testing were set high initially, since genetic testing was very expensive at the time (Manahan et al., 2019). Although cost of testing has decreased and guidelines are constantly changing, accurate calculation of carrier probabilities, given family history, is essential in supporting the decision for further testing, preventative treatment, or family planning (Chen et al., 2004).

Existing Mendelian models consider a relatively narrow subset of cancers and genes. For example, BRCAPRO, available in the BayesMendel R package (Chen et al., 2004), considers two cancers (breast and ovarian) and two genes (BRCA1 and BRCA2). Boadicea v4 Beta (Centre for Cancer Genetic Epidemiology, 2020) considers BRCA1, BRCA2, PALB2, CHEK2, and ATM mutations in the same cancers. To comprehensively incorporate cancers, genes, and their interactions, we introduce PanelPRO, an R package which aims to efficiently and flexibly scale to the demands of germline panel testing. The newly developed package has the following key advantages:

• Customizable model specification, including the choice of genes and cancers included in the model;

• Customizable model parameters, including the allele frequencies and penetrances;

• Accurate default parameter estimates, curated from an extensive literature search;

• Flexibility to incorporate cancer risk modifiers such as prophylactic surgeries;

• Comprehensive user input checks for pedigrees;

• Speed of computation, through an optimized C++ implementation of an efficient algorithm. (Madsen et al., 2018)

In general, PanelPRO can handle models with ${\displaystyle K}$ genes and ${\displaystyle R}$ cancers, where ${\displaystyle K}$ and ${\displaystyle R}$ are arbitrary (subject to reasonable run-times and memory constraints). It is intended that ${\displaystyle K}$ and ${\displaystyle R}$ become larger as more research on gene cancer associations becomes available. The package contains a comprehensive collection of functions designed to efficiently calculate carrier probabilities and future cancer risk for individuals, given detailed information about their family history.

PanelPRO is designed to be back-compatible with the existing BayesMendel package; individual models within that package (for example, BRCAPRO, MMRPRO, BRCAPRO5, or BRCAPRO6) can be called directly from PanelPRO by passing this model specification to the main function call. We expect that users of BayesMendel will migrate to this generalized and customizable enhancement, and that PanelPRO will lead to new users interested in broader cross syndrome modeling in the current landscape for cancer clinical risk assessment. In the current release, there are minor differences in how BayesMendel and PanelPRO deal with peer-reviewed data, in particular, for cancer penetrance calculations.

The PanelPRO R package is freely available for research purposes. The BayesMendel R package is likewise freely available for research purposes, and is currently licensed for clinical commercial use to CRA Health, CancerGene Connect, Progeny, FamHis, MagView, Igentify, CancerIQ, and Finch genetics. Family history has long been understood to be a key component for identifying risk and preventing heritable diseases, and clinical tools such as the ones which license BayesMendel are becoming more readily available (Welch et al., 2018). For BayesMendel, we leave clinical integration to the software licensees, including the integration of electronic medical records such as EPIC. We envision a similar dissemination plan for PanelPRO.

Methods: package workflow

The workflow of the package includes four main parts: the input, including user and model input; pre-processing of the inputs, including user input checks and a database build; running the peeling-paring algorithm; and outputting the results. Figure 1 shows the workflow. Additionally, Appendix 1—figure 1 shows detailed sub-routines within the package.

Figure 1

Download asset Open asset

Input

User input

The main input from the user is their pedigree. This is in the form of an R data.frame which contains detailed information about known family members, such as their ages and previous cancer diagnoses. The pedigree structure is defined by the ID, MotherID and FatherID columns. Previous cancer diagnoses and their ages of diagnosis are stored in the isAff* and Age* columns, respectively, where * represents a cancer type, designated according to a standard nomenclature of two- to four-letter tags which are also used in visualizations. Cancers currently considered in PanelPRO are listed in Appendix 1—table 1. Note, this list will be expanded for future versions of the package as more information on risk for various genes and cancers becomes available in the literature. Risk modifiers, such as prophylactic surgeries, can be incorporated to adjust the likelihood calculation. Previous genetic testing history can also be incorporated. The current version of the package, v0.2.0, contains thirteen sample pedigrees, called test_fam_X, where X goes from 1 to 12, and err_fam_1. The test_fam_X pedigrees provide realistic and extreme examples of the data that can be included in the user input, whereas err_fam_1 is an example of a family that does not pass PanelPRO’s preprocessing pedigree check and therefore cannot be evaluated by the model. A clipped version (with a subset of the necessary columns) of test_fam_1 pedigree is shown below. Key figures relevant to the pedigree can be found in Appendix 1—table 2.

head(test_fam_1)
##		ID	Sex	MotherID	FatherID	isProband	CurAge	isAffBC	isAffBC	AgeBC	AgeOC	isDead
##	1	1	0	NA	NA	0	93	1	0	65	NA	1
##	2	2	1	NA	NA	0	80	0	0	NA	NA	1
##	3	3	0	1	2	0	72	1	1	40	NA	0
##	4	4	1	1	2	0	65	0	0	NA	NA	1
##	5	5	1	1	2	0	65	0	0	NA	NA	0
##	6	6	0	1	2	1	55	0	0	NA	NA	0

The full specification of the pedigree structure is shown in Table 1. The family tree pedigree can also be visualized by using the visPed package as in Figure 2. This external package is available through https://github.com/bayesmendel/visPed version 0.1.0 (Lee, 2021) and is based on the kinship2 package available in CRAN (Sinnwell et al., 2014). PanelPRO itself does not contain pedigree plotting functionality. However, users can easily acquire the visPed package separately.

Table 1

Column	Definition	Value
ID	Unique numeric identifier of each individual	Non-repeated strictly positive integer
MotherID	ID of one’s mother	Strictly positive integer or NA (missing)
FatherID	ID of one’s father	Strictly positive integer or NA (missing)
Sex	Sex of the individual: 1 for male, 0 for female	One of {0, 1}
isProband	Indicates the proband or counselee by 1 and 0 otherwise – multiple probands can be specified	One of {0, 1)
CurAge	Age of censoring: either the current age or death age, depending on isDead status	Positive integer or NA (missing)
isAff*	Affection status of cancer *	One of {0, 1}
Age*	Affection age of cancer *	Positive integer or NA (missing)
isDead	Whether someone has died	One of {0, 1, NA}
race	Race of individual (used to modify penetrance)	One of All_Races, AIAN, Asian, Black, White, Hispanic, WH, WNH, NA
Ancestry	Ancestry of individual (used to modify allele frequencies)	One of AJ, nonAJ, Italian, NA
Twins	Identifies siblings who are identical twins or multiple births	Each set is identified by a unique integer, and 0 otherwise
riskmod	Preventative interventions which modify penetrance	List, combination of "mastectomy", "hysterectomy", and "oophorectomy"
InterAge	Age of each preventative interventions	List, combination of integers
Gene name from GENE_TYPES	Germline testing result	One of {0, 1, NA}
Marker name from CK14, CK5.6, ER, PR, HER2, MSI	Marker testing result	One of {0, 1, NA}

Figure 2

Download asset Open asset

Prophylactic surgeries (mastectomy, oophorectomy and hysterectomy) act as risk modifiers. They are specified in a column of lists in the user input pedigree. Including these risk modifiers changes the resulting carrier probability and future risk outputs (see the Output section). Previous history of biomarker testing for breast and colorectal cancers can also be included in the model.

Model input

Calculation of carrier probabilities requires information about allele frequencies and penetrances for the mutations and cancers requested in the function call. They are derived from peer-reviewed studies whose results are cataloged in the PanelPRODatabase. At each function call, the code extracts the appropriate subset of gene-cancer combinations. These combinations are specified in the main PanelPRO function call, where the user should indicate the cancers for which family history in the pedigree should be used, as well as the genes for which carrier probabilities are requested.

PanelPRO(pedigree = test_fam_1, 
                      cancers = c(‘Breast’, ‘Ovarian’), 
                      genes = c(‘BRCA1’, ‘BRCA2’, ‘ATM’, ‘MSH2’))

If no genes or cancers are specified, PanelPRO will default to all the supported genes in the version at that time, with all the cancers in the pedigree.

Users are free to change the database defaults for their own purposes. The structure of this database is an R list. A partial output is provided below.

str(PanelPRODatabase)
##	$ Penetrance	: num [1:18, 1:26, 1:8, 1:2, 1:94, 1:2] 3.98e-05 2.80e-07 0.00 5.00e-08 0.00 ...
##	..- attr(*, 'dimnames')=List of 6
##	.. ..$ Cancer	: chr [1:18] 'Brain' 'Breast' 'Cervical' 'Colorectal' ...
##	.. ..$ Gene	: chr [1:26] 'APC_hetero_anyPV' 'ATM_hetero_anyPV' 'BARD1_hetero_anyPV' ...
##	.. ..$ Race	: chr [1:8] 'All_Races' 'AIAN' 'Asian' 'Black' ...
##	.. ..$ Sex	: chr [1:2] 'Female' 'Male
##	.. ..$ PenetType	: chr [1:2] 'Net' 'Crude
##	$ AlleleFrequency	: num [1:24, 1:3] 1.45e-04 1.90e-03 3.41e-04 2.17e-05 1.37e-02 ...
##	..- attr(*, 'dimnames')=List of 2
##	.. ..$ Gene	: chr [1:24] 'APC_anyPV' 'ATM_anyPV' 'BARD1_anyPV' 'BMPR1A_anyPV' ...
##	.. ..$ Ancestry	: chr [1:3] 'AJ' 'nonAJ' 'Italian'

In the current PanelPRO database, cancer penetrances are taken from data included in the BayesMendel package when available: the BRCA1 and BRCA2 estimates for the probability of developing breast or ovarian cancer (Chen et al., 2020); the MLH1, MSH2, and MSH6 estimates for the probability of developing colorectal or endometrial cancer (Wang et al., 2020; Felton et al., 2007); and the CDKN2A estimates for the probability of developing melanoma (Wang et al., 2010; Begg et al., 2005; Bishop, 2002). All other cancer penetrances are pulled from the All Syndromes Known to Man Evaluator (ASK2ME) clinical tool (Braun et al., 2018).

For allele frequencies, we use the non-Ashkenazi, Ashkenazi Jewish, and Italian BRCA1 and BRCA2 allele frequency estimates from BRCAPRO (Chen et al., 2004; Antoniou et al., 2002); for MLH1, MSH2, and MSH6, we use the allele frequency estimates from MMRpro (Chen et al., 2004; Chen et al., 2006); and for CDKN2A, we use the allele frequency estimate from Melapro (Chen et al., 2004; Berwick, 2006). Allele frequency estimates for ATM, CHEK2, and PALB2 are taken from Lee et al., 2016. The allele frequencies of the remaining genes are estimated based on a 25-gene panel study of 252,223 individuals (Rosenthal et al., 2017) that did not adjust for ascertainment. In this case, we rescale the reported estimates by the ratio of the ascertained and unascertained allele frequencies for a gene reported in both our existing database and the study.

New genes and cancers will be added to PanelPRO based on regular literature reviews as conducted in ASK2ME (Braun et al., 2018). The ASK2ME approach identifies best-available studies that adjust for ascertainment; since many papers report odds ratios or relative risks, it then calculates absolute age-specific cancer penetrances when necessary.

The user can also select other options in the function call which are relevant at run-time. Examples include the maximum number of simultaneous gene mutations considered for a given individual, whether a parallelized version of the algorithm is performed, and the number of imputations in case of missing age data (see the Missing Data section). Many of the useful options are listed in Table 2.

Table 2

Option	Default value	Possible values	Description
max.mut	NULL	Integers up to the number of genes	Number of maximum simultaneous mutations, also known as the paring parameter. If no integer has been input, it re-defaults to 2.
iterations	20	Integers from 1 upwards	In case of missing current or cancer ages in the pedigree, this is the number of times those ages will be imputed.
parallel	TRUE	TRUE or FALSE	If age imputations are needed, this parameter can be set to utilize multiple cores on one’s machine.
net	FALSE	TRUE or FALSE	Determines whether net or crude penetrances are used to compute future risk of cancer. Net penetrances exclude all other causes of death, apart from the affected cancer.
age.by	5	Integers from one upwards	The intervals of age used to report the future risk of cancer.

Passing these options to the function call is simple, as shown below.

PanelPRO(pedigree = test_fam_1, 
                      cancers = c(‘Breast’, ‘Ovarian’),
                      genes = c(‘BRCA1’, ‘BRCA2’, ‘ATM’, ‘MSH2’),
                      max.mut = 1,
                      parallel = FALSE)

Instead of specifying a set of cancers and genes, users can call models corresponding to those in the BayesMendel package, as well as other predefined models. For example, the two calls below are equivalent.

bayesMendelCall <- BRCAPRO6(pedigree = test_fam_1)
panelProCall <- PanelPRO(pedigree = test_fam_1,
                                                    cancers = c(‘Breast’, ‘Ovarian’),
                                               genes = c(‘BRCA1’, ‘BRCA2’, ‘MLH1’, ‘MSH2’, ‘MSH6’, ‘CDKN2A’))
all.equal(bayesMendelCall, panelProCall)
## (1) TRUE

Preprocessing

Pedigree check

First, PanelPRO checks the structure of the user-supplied R data.frame containing the family history to be evaluated, using a call to the checkFam function. A description can be found in Table 3. Messages or warnings are given to the user if values have been automatically changed to rectify conflicts. For example, test_fam_1 contains some ancestry and race inconsistencies.

Table 3

Category	Name	Description
Pre-processing	checkFam	Checks family structure as defined by the user. The inputs are a data.frame specifying the pedigree and a built database returned by buildDatabase. The output is a modified data.frame pedigree and list of imputed ages, if missing ages were imputed (see the Missing Data section).
Pre-processing	buildDatabase	Subsets the internal database PanelPRODatabase depending on the cancers and genes selected. The input is the list PanelPRODatabase. The output is another list which is a subset of PanelPRODatabase.
Algorithm	PanelPROCalc	Estimates the posterior carrier probabilities and future risks of the proband. The inputs are the outputs of checkFam. The outputs are lists of posterior probabilities and future risks for the proband.
Main function	PanelPRO	Runs main function. The inputs are the user-specified pedigree, a vector of cancers in the model, a vector of genes in the model, and other optional parameters. The output is a list of estimates of posterior carrier probabilities for each genotype, along with future cancer risks and ranges for each of these.

checkFam(test_fam_1)
## Your model has two cancers - Breast, Ovarian and 24 genes - APC_hetero_anyPV, ATM_hetero_anyPV ...
## Germline testing results for BRCA1 are assumed to be for default variant BRCA1_hetero_anyPV.
## Germline testing results for BRCA2 are assumed to be for default variant BRCA2_hetero_anyPV.
## ID 3 ’s Ancestry has been changed to nonAJ to meet heredity consistency
## ID 10 ’s race has been changed to All_Races to meet heredity consistency
## ID 9 ’s Ancestry has been changed to nonAJ to meet heredity consistency
## ID 16,17 ’s Ancestry has been changed to nonAJ to meet heredity consistency
## ID 29,30 ’s Ancestry has been changed to nonAJ to meet heredity consistency
## ID 33,34 ’s race has been changed to All_Races to meet heredity consistency
## ID 33,34 ’s Ancestry has been changed to nonAJ to meet heredity consistency

Errors are given if inconsistencies or ambiguities in the pedigree cannot be resolved such that the pedigree can be safely passed into downstream functions. Most of the checks are for the presence of required information; whether the pedigree variables have values in the expected range/set; and for consistency between cancers and sex and in terms of features with hereditary assumptions among parents/children and twins. The pedigree is also checked for ‘loops’, which PanelPRO currently does not support. For example, within err_fam_1, there are two sets of male and female siblings (four individuals) who have mated with the corresponding siblings in another family, as shown in Figure 3. This mating configuration results in a loop. For a more detailed definition of loops, see Fernando et al., 1993. In addition, disconnected family members are detected and removed from the pedigree if they will not influence the counselee’s results.

Figure 3

Download asset Open asset

Build database

Depending on the configuration of the model requested (cancers in the family, genes considered), a subset of PanelPRODatabase or a user-modified database will be created and passed through for further calculation by the buildDatabase function. A description of this function can be found in Table 3 .

Output

For each proband in the pedigree, the output consists of:

• estimates of carrier probabilities,

• lower and upper bound estimates of carrier probabilities if imputations were made for missing data,

• estimates of future risks of cancers in 5-year intervals (the user can also change the length of the intervals),

• lower and upper bound estimates of future risks of cancers in 5-year intervals if imputations were made for missing data.

Messages or warnings generated from checkFam have been omitted in the example below for brevity.

output <- PanelPRO(pedigree = test_fam_1, 
                                          cancers = c(‘Breast’, ‘Ovarian’), 
                                          genes = c(‘BRCA1’, ‘BRCA2’, ‘ATM’, ‘MSH2’), 
                                          max.mut = 2, 
                                          parallel = FALSE)

## Your model has two cancers - Breast, Ovarian and four genes - BRCA1_hetero_anyPV ...

output
##	$posterior.prob
##	$posterior.prob$‘6‘
##		genes				estimate	lower	upper
##	1	noncarrier				6.895857e-01	6.852634e-01	6.901750e-01
##	2	BRCA1_hetero_anyPV				3.028050e-01	3.009048e-01	3.030639e-01
##	3	BRCA2_hetero_anyPV				3.486877e-04	2.817221e-04	4.216045e-04
##	4	ATM_hetero_anyPV				4.027689e-03	3.904178e-03	5.532214e-03
##	5	MSH2_hetero_anyPV				9.814770e-04	5.697486e-04	4.574016e-03
##	6	BRCA1_hetero_anyPV.BRCA2_hetero_anyPV				1.462717e-04	1.181799e-04	1.768601e-04
##	7	BRCA1_hetero_anyPV.ATM_hetero_anyPV				1.689866e-03	1.638046e-03	2.321095e-03
##	8	BRCA2_hetero_anyPV.ATM_hetero_anyPV				7.125399e-07	6.081245e-07	9.481190e-07
##	9	BRCA1_hetero_anyPV.MSH2_hetero_anyPV				4.118459e-04	2.390780e-04	9.481190e-07
##	10	BRCA2_hetero_anyPV.MSH2_hetero_anyPV				4.118459e-04	1.361573e-07	9.481190e-07
##	11	ATM_hetero_anyPV.MSH2_hetero_anyPV				2.541503e-06	1.637872e-06	9.481190e-07
##
##
##	$future.risk
##	$future.risk$‘6‘
##	$future.risk$‘6‘$Breast
##
##		ByAge	estimate	lower	upper
##	1	60	0.04694233	0.04691714	0.04707834
##	2	65	0.09340834	0.09336108	0.09367413
##	3	70	0.13709254	0.13702686	0.13747579
##	4	75	0.17464537	0.17456501	0.17513057
##	5	80	0.20483919	0.20474732	0.20541009
##	6	85	0.22687210	0.22677195	0.22750817
##	7	90	0.23927170	0.23916714	0.23994509
##
##	$future.risk$‘6‘$Ovarian
##		ByAge	estimate	lower	upper
##	1	60	0.03600079	0.03598267	0.17954734
##	2	65	0.07186345	0.07183188	0.17954734
##	3	70	0.10464159	0.10460004	0.10477485
##	4	75	0.13238354	0.13233432	0.13253217
##	5	80	0.15520230	0.13233432	0.15536227
##	6	85	0.17183044	0.17177152	0.15536227
##	7	90	0.17960786	0.17954734	0.15536227

The package includes the function visRisk to visualize the output graphically. Figure 4 demonstrates this usage for test_fam_1. The visRisk function was implemented using the plotly (Sievert, 2020) package, so that the output can be rendered interactively and display the exact probabilities upon hovering.

Figure 4

Download asset Open asset

Additional examples

In this section, we display some of the other test pedigrees included in PanelPRO and their corresponding output from the visRisk function, as well as a comparison to some other platforms and models. We compared PanelPRO to: BRCAPRO and MMRPRO in BayesMendel (Chen et al., 2004), CanRisk (Lee et al., 2019; Carver et al., 2021), IBIS (Tyrer et al., 2004), and PREMM-5 (Kastrinos et al., 2017), which all support different cancers, genes and model assumptions. Table 4 summarizes the supported inputs and outputs of each model. If a particular model or platform does not support certain cancers, the irrelevant family history is simply omitted from input. For brevity, Figures 5–9 provide visualizations of the pedigrees of these additional examples, and the corresponding model outputs are reported in as figure supplements. In all cases, we used the default model settings (if any). Many of the aforementioned platforms have long reports as outputs, so we have only included the portions concerned with carrier probabilities and future risks. The same information contained in the PanelPRO sample pedigrees is input to the other models; however not all of the features are used by these other platforms. Conversely, there are some inputs for the other models that PanelPRO does not include.

Table 4

Model or platform name	Version	Supported cancer input types	Supported gene carrier probability outputs	Supported future cancer risk outputs
PanelPRO	0.2.0	Brain, breast, cervical, colorectal, endometrial, gastric, kidney, leukemia, melanoma, ovarian, osteosarcoma, pancreatic, small intestine, soft tissue sarcoma, thyroid, urinary bladder, hepatobiliary	APC, ATM, BARD1, BMPR1A, BRCA1, BRCA2, BRIP1, CDH1, CDK4, CDKN2A, CHEK2, EPCAM, MLH1, MSH2, MSH6, MUTYH, NBN, PALB2, PMS2, PTEN, RAD51C, RAD51D, STK11, TP53	same as cancer inputs
BRCAPRO	2.1–7	Breast, ovarian	BRCA1, BRCA2	same as cancer inputs
MMRPRO	2.1–7	Colorectal, endometrial	MLH1, MSH2, MSH6	same as cancer inputs
IBIS	0.8b	Breast	NA	Breast
CanRisk	1.2.3	Breast, contralateral breast, ovarian, prostate, pancreatic	BRCA1, BRCA2, PALB2, CHEK2, ATM, RAD51D, RAD51C, BRIP1	Breast, ovarian
PREMM-5	NA	Colorectal, endometrial, other (group of ovarian, stomach, small intestine, urinary tract/bladder/kidney, bile ducts, brain, pancreas, sebaceous gland skin)	MLH1, MSH2, MSH6, PMS2, EPCAM	NA

Figure 5 with 3 supplements see all

Download asset Open asset

Figure 6 with 5 supplements see all

Download asset Open asset

Figure 7 with 3 supplements see all

Download asset Open asset

Figure 8 with 5 supplements see all

Download asset Open asset

Figure 9 with 5 supplements see all

Download asset Open asset

Notably, all the cancers and genes supported by these other models are a subset of those supported in PanelPRO, except for PREMM-5, which takes into consideration other cancers associated with Lynch syndrome which are not currently included in PanelPRO (bile duct and sebaceous gland). However, PREMM-5 does not provide the associated future risk for these additional cancers, only carrier probabilities for MLH1, MSH2, MSH6, PMS2, and EPCAM.

Comparing PanelPRO with BRCAPRO and MMRPRO, we see that PanelPRO offers carrier probability estimates for a larger set of genes, as well as a graphical output of the future risk. IBIS does not give any estimates for carrier probabilities; however, it gives a summary of the future risks in text format, relative to population averages. Finally, PREMM-5 gives an estimate of carrying any of five genes (MLH1, MSH2, MSH6, PMS2, or EPCAM), whilst PanelPRO is able to give estimates for each of those individual genes. PREMM-5 also does not give estimates for future risks of cancer.

Two pedigrees that illustrate the differences between PanelPRO and PREMM-5 are test_fam_7 (Figure 5) and test_fam_11 (Figure 9). For test_fam_7, PanelPRO estimates a 42.3% probability of carrying an MLH1, MSH2, MSH6, PMS2, or EPCAM mutation (when excluding the possibility of multiple simultaneous gene mutations), compared to 32% for PREMM-5. This pedigree only contains family history of colorectal and endometrial cancers, which PREMM-5 uses as key risk factors, leading to similar results. In contrast, the mutation probability estimates between the same two models for test_fam_11 are quite different. This pedigree is an extreme example that contains history of endometrial, small intestine, ovarian, and pancreatic cancers, but PREMM-5 groups the latter three cancers into a single risk factor for any other Lynch syndrome-associated cancers. The differences in the model approaches and assumptions result in PanelPRO giving a 93% estimate for a mutation in any of the aforementioned genes (without multiple simultaneous gene mutations), while PREMM-5 returns 3.2%. test_fam_11 is an extreme pedigree, but it nonetheless illustrates the flexibility of PanelPRO for incorporating very detailed pedigree information with a high clinical impact.

PanelPRO is a highly flexible package which provides an interface to efficiently calculate carrier probabilities for a wide array of cancer susceptibility genes, as well as future cancer risks. It is designed for R users. Similarly to the BayesMendel package, it can provide the computational engine behind clinical and counseling decision support tools.

It excels in being fully customizable. Any combination of the 24 genes and 18 cancers currently in version 0.2.0 of the package can be included in the model. New genes and cancers can easily be added, and in fact the code allows for an arbitrary number of genes and cancers. Risk modifiers have been included for certain procedures, and more can be added as additional information becomes available. The user can also change the internal database of parameter values.

The package includes a comprehensive check on the input pedigree to ensure users are informed of potentially inconsistent or infeasible data entries. When it is possible to do so safely, the data is automatically remedied and the user is then notified. Otherwise, the program will halt with an informative error message. Once the pedigree is pre-processed, the posterior probabilities are calculated efficiently. For example, test_fam_1, which has 19 members and family history of 2 cancers, runs with all the default settings in a few seconds as shown in Figure 10. The polynomial run-time of the peeling-paring algorithm is alleviated with PanelPRO’s Rcpp implementation. Even when relaxing the maximum mutations (paring) parameter, the C++ implementation is able to handle the calculations efficiently. Run-times in these ranges are certainly appropriate for clinical use, as well as use in a research setting where possibly hundreds of pedigrees have to be processed through PanelPRO. Moreover, the peeling-paring algorithm run-time scales linearly in the number of family members in the pedigree and can handle hundreds of members in an inter-generational configuration easily.

Figure 10

Download asset Open asset

PanelPRO has two main limitations. Firstly, the initial release does not handle pedigrees which contain loops. This additional functionality would be desirable in future releases, although loops in pedigrees do not happen frequently. Several studies suggest either exact or approximate computations for pedigrees with loops, see Stricker et al., 1995 and Totir et al., 2009. Secondly, the polynomial scaling of peeling-paring as a function of the number of genes considered becomes significant when many genes are incorporated. This issue is of concern because we strive for future releases to contain far more genes than 24 as data becomes available. Alternative algorithms which have different time complexity properties, such as the Lander-Green family of algorithms (Lander and Green, 1987), should be explored. These algorithms scale linearly in terms of the number of genes considered, but are exponential in the number of family members in the pedigree (Gao et al., 2009). A future objective for this package is to contain a choice of the carrier probability calculation method, and ideally an automatic selection of the one which is most efficient, depending on family size and total number of genes. Appropriate thresholds of these two parameters need to be determined by a comprehensive benchmarking exercise.

This manuscript introduces PanelPRO, an innovative multi-gene multi-cancer Mendelian model. Software for this model, including the model parameter database, is available to download for research use; https://projects.iq.harvard.edu/bayesmendel/panelpro.

1. 1. Lee G
   2. Liang JW
   3. Zhang Q
   4. Huang T
   5. Choirat C
   6. Parmigiani G
   7. Braun D

   (2021) PanelPRO R Package

   ID projects.iq.harvard.edu/bayesmendel/panelpro. PanelPRO R Package.

   https://projects.iq.harvard.edu/bayesmendel/panelpro

1. 1. Antoniou AC
   2. Pharoah PD
   3. McMullan G
   4. Day NE
   5. Stratton MR
   6. Peto J
   7. Ponder BJ
   8. Easton DF

   (2002) A comprehensive model for familial breast Cancer incorporating BRCA1, BRCA2 and other genes

   British Journal of Cancer 86:76–83.

   https://doi.org/10.1038/sj.bjc.6600008

   • PubMed
   • Google Scholar
2. 1. Begg CB
   2. Orlow I
   3. Hummer AJ
   4. Armstrong BK
   5. Kricker A
   6. Marrett LD
   7. Millikan RC
   8. Gruber SB
   9. Anton-Culver H
   10. Zanetti R
   11. Gallagher RP
   12. Dwyer T
   13. Rebbeck TR
   14. Mitra N
   15. Busam K
   16. From L
   17. Berwick M

   (2005) Lifetime risk of melanoma in CDKN2A mutation carriers in a Population-Based sample

   JNCI: Journal of the National Cancer Institute 97:1507–1515.

   https://doi.org/10.1093/jnci/dji312

   • Google Scholar
3. 1. Berwick M

   (2006) The prevalence of CDKN2A Germ-Line mutations and relative risk for cutaneous malignant melanoma: an international Population-Based study

   Cancer Epidemiology Biomarkers & Prevention 15:1520–1525.

   https://doi.org/10.1158/1055-9965.EPI-06-0270

   • Google Scholar
4. 1. Bishop DT

   (2002) Geographical variation in the penetrance of CDKN2A mutations for melanoma

   CancerSpectrum Knowledge Environment 94:894–903.

   https://doi.org/10.1093/jnci/94.12.894

   • Google Scholar
5. 1. Biswas S
   2. Atienza P
   3. Chipman J
   4. Hughes K
   5. Barrera AMG
   6. Amos CI
   7. Arun B
   8. Parmigiani G

   (2013) Simplifying clinical use of the genetic risk prediction model BRCAPRO

   Breast Cancer Research and Treatment 139:571–579.

   https://doi.org/10.1007/s10549-013-2564-4

   • Google Scholar
6. 1. Braun D
   2. Yang J
   3. Griffin M
   4. Parmigiani G
   5. Hughes KS

   (2018) A clinical decision support tool to predict Cancer risk for commonly tested Cancer-Related germline mutations

   Journal of Genetic Counseling 27:1187–1199.

   https://doi.org/10.1007/s10897-018-0238-4

   • PubMed
   • Google Scholar
7. 1. Carver T
   2. Hartley S
   3. Lee A
   4. Cunningham AP
   5. Archer S
   6. Babb de Villiers C
   7. Roberts J
   8. Ruston R
   9. Walter FM
   10. Tischkowitz M
   11. Easton DF
   12. Antoniou AC

   (2021) CanRisk Tool—A Web Interface for the Prediction of Breast and Ovarian Cancer Risk and the Likelihood of Carrying Genetic Pathogenic Variants

   Cancer Epidemiology Biomarkers & Prevention 30:469–473.

   https://doi.org/10.1158/1055-9965.EPI-20-1319

   • Google Scholar
8. Software

   1. Centre for Cancer Genetic Epidemiology

   (2020) BOADICEA Model Description

   BOADICEA Model Description.

   https://ccge.medschl.cam.ac.uk/boadicea/boadicea-model/
9. 1. Chen S
   2. Wang W
   3. Broman KW
   4. Katki HA
   5. Parmigiani G

   (2004) BayesMendel: an R Environment for Mendelian Risk Prediction

   Statistical Applications in Genetics and Molecular Biology 3:1–19.

   https://doi.org/10.2202/1544-6115.1063

   • Google Scholar
10. 1. Chen S
    2. Wang W
    3. Lee S
    4. Nafa K
    5. Lee J
    6. Romans K
    7. Watson P
    8. Gruber SB
    9. Euhus D
    10. Kinzler KW
    11. Jass J
    12. Gallinger S
    13. Lindor NM
    14. Casey G
    15. Ellis N
    16. Giardiello FM
    17. Offit K
    18. Parmigiani G

    (2006) Prediction of germline mutations and Cancer risk in the lynch syndrome

    Jama 296:1479–1487.

    https://doi.org/10.1001/jama.296.12.1479

    • PubMed
    • Google Scholar
11. 1. Chen J
    2. Bae E
    3. Zhang L
    4. Hughes K
    5. Parmigiani G
    6. Braun D
    7. Rebbeck TR

    (2020) Penetrance of breast and ovarian Cancer in women who carry a BRCA1/2 Mutation and Do Not Use Risk-Reducing Salpingo-Oophorectomy: An Updated Meta-Analysis

    JNCI Cancer Spectrum 4:pkaa029.

    https://doi.org/10.1093/jncics/pkaa029

    • PubMed
    • Google Scholar
12. 1. Eddelbuettel D
    2. Francois R

    (2011) Rcpp: seamless r and c++ integration

    Journal of Statistical Software 40:1–18.

    https://doi.org/10.18637/jss.v040.i08

    • Google Scholar
13. 1. Eddelbuettel D
    2. Sanderson C

    (2014) RcppArmadillo: accelerating R with high-performance C++ linear algebra

    Computational Statistics & Data Analysis 71:1054–1063.

    https://doi.org/10.1016/j.csda.2013.02.005

    • Google Scholar
14. 1. Elston RC
    2. Stewart J

    (1971) A general model for the genetic analysis of pedigree data

    Human Heredity 21:523–542.

    https://doi.org/10.1159/000152448

    • PubMed
    • Google Scholar
15. 1. Felton KE
    2. Gilchrist DM
    3. Andrew SE

    (2007) Constitutive deficiency in DNA mismatch repair: is it time for lynch III?

    Clinical Genetics 71:499–500.

    https://doi.org/10.1111/j.1399-0004.2007.00801.x

    • PubMed
    • Google Scholar
16. 1. Fernando RL
    2. Stricker C
    3. Elston RC

    (1993) An efficient algorithm to compute the posterior genotypic distribution for every member of a pedigree without loops

    Theoretical and Applied Genetics 87:89–93.

    https://doi.org/10.1007/BF00223750

    • PubMed
    • Google Scholar
17. 1. Gao G
    2. Allison DB
    3. Hoeschele I

    (2009) Haplotyping methods for pedigrees

    Human Heredity 67:248–266.

    https://doi.org/10.1159/000194978

    • PubMed
    • Google Scholar
18. 1. Kastrinos F
    2. Uno H
    3. Ukaegbu C
    4. Alvero C
    5. McFarland A
    6. Yurgelun MB
    7. Kulke MH
    8. Schrag D
    9. Meyerhardt JA
    10. Fuchs CS
    11. Mayer RJ
    12. Ng K
    13. Steyerberg EW
    14. Syngal S

    (2017) Development and validation of the PREMM₅model for comprehensive risk assessment of lynch syndrome

    Journal of Clinical Oncology 35:2165–2172.

    https://doi.org/10.1200/JCO.2016.69.6120

    • PubMed
    • Google Scholar
19. 1. Lander ES
    2. Green P

    (1987) Construction of multilocus genetic linkage maps in humans

    PNAS 84:2363–2367.

    https://doi.org/10.1073/pnas.84.8.2363

    • PubMed
    • Google Scholar
20. 1. Lee AJ
    2. Cunningham AP
    3. Tischkowitz M
    4. Simard J
    5. Pharoah PD
    6. Easton DF
    7. Antoniou AC

    (2016) Incorporating truncating variants in PALB2, CHEK2, and ATM into the BOADICEA breast Cancer risk model

    Genetics in Medicine 18:1190–1198.

    https://doi.org/10.1038/gim.2016.31

    • PubMed
    • Google Scholar
21. 1. Lee A
    2. Mavaddat N
    3. Wilcox AN
    4. Cunningham AP
    5. Carver T
    6. Hartley S
    7. Babb de Villiers C
    8. Izquierdo A
    9. Simard J
    10. Schmidt MK
    11. Walter FM
    12. Chatterjee N
    13. Garcia-Closas M
    14. Tischkowitz M
    15. Pharoah P
    16. Easton DF
    17. Antoniou AC

    (2019) BOADICEA: a comprehensive breast Cancer risk prediction model incorporating genetic and nongenetic risk factors

    Genetics in Medicine 21:1708–1718.

    https://doi.org/10.1038/s41436-018-0406-9

    • PubMed
    • Google Scholar
22. Software

    1. Lee GK

    (2021) visPed, version bd17577

    GitHub .

    https://github.com/bayesmendel/visPed
23. 1. Madsen T
    2. Braun D
    3. Peng G
    4. Parmigiani G
    5. Trippa L

    (2018) Efficient computation of the joint probability of multiple inherited risk alleles from pedigree data

    Genetic Epidemiology 42:528–538.

    https://doi.org/10.1002/gepi.22130

    • PubMed
    • Google Scholar
24. 1. Manahan ER
    2. Kuerer HM
    3. Sebastian M
    4. Hughes KS
    5. Boughey JC
    6. Euhus DM
    7. Boolbol SK
    8. Taylor WA

    (2019) Consensus guidelines on genetic` testing for hereditary breast Cancer from the american society of breast surgeons

    Annals of Surgical Oncology 26:3025–3031.

    https://doi.org/10.1245/s10434-019-07549-8

    • PubMed
    • Google Scholar
25. 1. Nelson HD
    2. Pappas M
    3. Zakher B
    4. Mitchell JP
    5. Okinaka-Hu L
    6. Fu R

    (2014) Risk assessment, genetic counseling, and genetic testing for BRCA-related Cancer in women: a systematic review to update the U.S. preventive services task force recommendation

    Annals of Internal Medicine 160:255–266.

    https://doi.org/10.7326/M13-1684

    • PubMed
    • Google Scholar
26. 1. Plichta JK
    2. Griffin M
    3. Thakuria J
    4. Hughes KS

    (2016)

    What’s new in genetic testing for cancer susceptibility?

    Oncology 30:787–799.

    • PubMed
    • Google Scholar
27. 1. Rosenthal ET
    2. Bernhisel R
    3. Brown K
    4. Kidd J
    5. Manley S

    (2017) Clinical testing with a panel of 25 genes associated with increased Cancer risk results in a significant increase in clinically significant findings across a broad range of Cancer histories

    Cancer Genetics 218-219:58–68.

    https://doi.org/10.1016/j.cancergen.2017.09.003

    • PubMed
    • Google Scholar
28. Book

    1. Sievert C

    (2020)

    Interactive Web-Based Data Visualization with R, Plotly, and Shiny

    Chapman and Hall/CRC.

    • Google Scholar
29. 1. Sinnwell JP
    2. Therneau TM
    3. Schaid DJ

    (2014) The kinship2 R package for pedigree data

    Human Heredity 78:91–93.

    https://doi.org/10.1159/000363105

    • PubMed
    • Google Scholar
30. 1. Stricker C
    2. Fernando RL
    3. Elston RC

    (1995) An algorithm to approximate the likelihood for pedigree data with loops by cutting

    Theoretical and Applied Genetics 91:1054–1063.

    https://doi.org/10.1007/BF00223919

    • PubMed
    • Google Scholar
31. 1. Totir LR
    2. Fernando RL
    3. Abraham J

    (2009) An efficient algorithm to compute marginal posterior genotype probabilities for every member of a pedigree with loops

    Genetics Selection Evolution 41:52.

    https://doi.org/10.1186/1297-9686-41-52

    • Google Scholar
32. 1. Tyrer J
    2. Duffy SW
    3. Cuzick J

    (2004) A breast Cancer prediction model incorporating familial and personal risk factors

    Statistics in Medicine 23:1111–1130.

    https://doi.org/10.1002/sim.1668

    • PubMed
    • Google Scholar
33. 1. Wang W
    2. Niendorf KB
    3. Patel D
    4. Blackford A
    5. Marroni F
    6. Sober AJ
    7. Parmigiani G
    8. Tsao H

    (2010) Estimating CDKN2A carrier probability and personalizing Cancer risk assessments in hereditary melanoma using MelaPRO

    Cancer Research 70:552–559.

    https://doi.org/10.1158/0008-5472.CAN-09-2653

    • PubMed
    • Google Scholar
34. 1. Wang C
    2. Wang Y
    3. Hughes KS
    4. Parmigiani G
    5. Braun D

    (2020) Penetrance of colorectal Cancer among mismatch repair gene mutation carriers: a Meta-Analysis

    JNCI Cancer Spectrum 4:pkaa027.

    https://doi.org/10.1093/jncics/pkaa027

    • PubMed
    • Google Scholar
35. 1. Welch BM
    2. Wiley K
    3. Pflieger L
    4. Achiangia R
    5. Baker K
    6. Hughes-Halbert C
    7. Morrison H
    8. Schiffman J
    9. Doerr M

    (2018) Review and comparison of electronic Patient-Facing family health history tools

    Journal of Genetic Counseling 27:381–391.

    https://doi.org/10.1007/s10897-018-0235-7

    • PubMed
    • Google Scholar

Author details

1. Gavin Lee

   Swiss Data Science Center, ETH Zürich and EPFL, Lausanne, Switzerland

   Contribution

   Software, Investigation, Methodology, Writing - original draft

   Contributed equally with

   Jane W Liang

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2659-1163

2. Jane W Liang

   1. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States
   2. Department of Data Sciences, Dana-Farber Cancer Institute, Boston, United States

   Contribution

   Software, Formal analysis, Validation, Methodology, Writing - review and editing

   Contributed equally with

   Gavin Lee

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2302-3809

3. Qing Zhang

   Broad Institute of MIT and Harvard, Cambridge, United States

   Contribution

   Software, Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Theodore Huang

   1. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States
   2. Department of Data Sciences, Dana-Farber Cancer Institute, Boston, United States

   Contribution

   Conceptualization, Data curation, Software, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

5. Christine Choirat

   Swiss Data Science Center, ETH Zürich and EPFL, Lausanne, Switzerland

   Contribution

   Supervision, Investigation, Methodology, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

6. Giovanni Parmigiani

   1. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States
   2. Department of Data Sciences, Dana-Farber Cancer Institute, Boston, United States

   Contribution

   Conceptualization, Formal analysis, Supervision, Methodology, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

7. Danielle Braun

   1. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States
   2. Department of Data Sciences, Dana-Farber Cancer Institute, Boston, United States

   Contribution

   Conceptualization, Supervision, Methodology, Project administration, Writing - review and editing

   For correspondence

   bmendel@jimmy.harvard.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5177-8598

• 1,576

  views

• 164

  downloads

• 9

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.