Introduction

Genetic testing of patients with cancer to identify variants associated with an increased cancer risk and sensitivity to targeted therapies is becoming more common as broad testing criteria are integrated into clinical care guidelines (Goggins et al., 2020; Stoffel et al., 2019). American College of Medical Genetics (ACMG) provides a framework to integrate multiple types of evidence, including variant characteristics, disease epidemiology, clinical information, and functional classifications, to interpret variants in any gene (Richards et al., 2015). In silico variant effect predictors are also integrated into AMCG variant interpretation guidelines as supporting evidence to aid classification of variants. While numerous models have been developed, varied accuracy, poor agreement between models, and inflated performance on publicly available data have been reported (Cubuk et al., 2021; Jaffe et al., 2011; Wilcox et al., 2022). Recently developed variant effect predictors aim to overcome these limitations by incorporating deep-learning based protein structure predictions and by not training on human annotated datasets (Brandes et al., 2023; Cheng et al., 2023; Gao et al., 2023). However, post-development assessment of machine learning based variant effect predictors to determine accuracy on novel experimental datasets and suitability for clinical use are limited.

Variants that cannot be classified as either pathogenic or benign are categorized as variants of uncertain significance (VUSs). However, while pathogenic and benign variants identified during genetic testing are clinically actionable, VUSs are the cause of deep uncertainty for patients and their health care providers as an unknown fraction are functionally deleterious and therefore, likely pathogenic. For example, individuals with germline VUSs in a pancreatic cancer susceptibility gene would not be eligible for clinical surveillance programs that are associated with improved patient outcomes, unless they otherwise meet family history criteria (Goggins et al., 2020; Stoffel et al., 2019). Similarly, patients with breast or pancreatic cancer and a germline BRCA2 VUS would not be eligible for treatment with olaparib, a poly (ADP-ribose) polymerase inhibitor (Golan et al., 2019; Tutt et al., 2021). Reclassification of VUSs into pathogenic or benign strata has real-world, life-or-death consequences that necessitate a high degree of accuracy.

Germline VUSs in hereditary cancer genes are a frequent finding in patients with cancer and frequently can be reclassified as pathogenic on the basis of in vitro functional characterization (Kimura et al., 2022). In patients with pancreatic ductal adenocarcinoma (PDAC), germline CDKN2A VUSs affecting p16^INK4a, most often rare missense variants, are found in up to 4.3% of patients (Chaffee et al., 2018; Kimura et al., 2021; McWilliams et al., 2018; Roberts et al., 2016; Shindo et al., 2017; Zhen et al., 2015). As functional data from well-validated in-vitro assays are incorporated into ACMG variant interpretation guidelines, we recently determined the functional consequence of 29 CDKN2A VUSs identified in patients with PDAC using an in vitro cell proliferation assay (Kimura et al., 2022; Richards et al., 2015). We found that over 40% of VUSs assayed were functionally deleterious and could reclassified as likely pathogenic.

Functional characterization is time-consuming, expensive, and requires technical and scientific expertise. These limitations hinder assessment of in silico variant effect predictors and patient access to functional data that may allow reclassification of VUSs into clinically actionable strata. As CDKN2A VUSs will continue to be identified in patients with cancer undergoing genetic testing, we developed a multiplexed functional assay to provide a broad interpretation framework for CDKN2A variants. We characterized all possible CDKN2A missense variants and compared our functional classifications to including recently developed in silico models based on machine learning to determine the accuracy of variant effect predictions.

Results

Functional characterization of CDKN2A missense variants

We utilized a codon optimized CDKN2A sequence for our multiplexed functional assay (Appendix 1-table 1). Expression of codon optimized CDKN2A or three synonymous CDKN2A variants, p.L32L, p.G101G, and p.V126V, in PANC-1, a PDAC cell line with a homozygous deletion of CDKN2A, resulted in significant reduction is cell proliferation (P value < 0.0001; Figure 1-figure supplement 1A). Conversely, expression of three pathogenic variants, p.L32P, p.G101W, and p.V126D, in PANC-1 cells did not result in any significant changes in cell proliferation. To determine if there were unappreciated selective effects during in vitro culture, we generated a CellTag library based on the pLJM1 plasmid that contained twenty non-functional 9 base pair barcodes of equal representation. We then transduced PANC-1 cells stably expressing codon optimized CDKN2A with the CellTag library and determined representation of each barcode in the cell pool, before and after in vitro culture. We found no statistically significant changes in barcode representation, indicating that representation of a pool of functionally neutral variants is stable over a period of in vitro culture representing our assay time course (Figure 1-figure supplement 1B).

We next determined whether we could identify functionally deleterious CDKN2A variants at a single residue when all amino acid variants were assayed simultaneously. We generated lentiviral expression libraries for two CDKN2A amino acid residues, p.V126 and p.R144, that include pathogenic and benign variants, respectively. Each lentiviral expression library contained all amino acid variants (19 missense and 1 synonymous variant) at a single residue. We then transduced PANC-1 cells with each of the lentiviral expression libraries and determined the representation of each variant in the resulting cell pool before and after a period of in vitro culture (Figure 1A, B). Synonymous variants, p.V126V and p.R1441R, as well as a previously reported benign variant, p.R144C, either decreased or maintained their representation in the cell pool during in vitro culture. Representation of a previously reported pathogenic variant, p.V126D, increased in the cell pool. Notably, several other variants including p.V126R, p.V126W, p.V126K, and p.V126Y, increased in representation, suggesting that additional amino acid changes at this residue are functionally deleterious (Figure 1A).

Figure 1.

Next, to functionally characterize 2,964 CDKN2A missense variants, we generated 156 CDKN2A lentiviral expression libraries where each library contained all possible amino acid substitutions at a single residue. PANC-1 cells were then transduced with each of the lentiviral expression libraries and representation of each CDKN2A variant in the resulting cell pool determined before and after a period of in vitro culture. Variant read counts were then analyzed using a gamma generalized linear model and variants with statistically significant P values were classified as functionally deleterious. Variants with P values that did not reach statistical significance were classified as functionally deleterious.

We found that 1,182 of 2,964 missense variants (39.9%) characterized in our assay were functionally deleterious and that 1,782 variants (60.1%) were functionally neutral (Figure 2A, Appendix 1-table 2). In general, our results were consistent with previously reported classifications. Thirty-two variants identified in patients with cancer and previously reported to be functionally deleterious in published literature and/or reported in ClinVar as pathogenic or likely pathogenic were characterized as functionally deleterious in our assay (Figure 2B, Appendix 1-table 3) (Chaffee et al., 2018; Chang et al., 2016; Horn et al., 2021; Hu et al., 2018; Kimura et al., 2022; McWilliams et al., 2018; Roberts et al., 2016; Zhen et al., 2015). Of 162 benign variants, including 156 synonymous variants and six missense variants previously reported to be functionally neutral in published literature and/or reported in ClinVar as benign or likely benign, all were characterized as functionally neutral in our assay (Figure 2B, Appendix 1-table 3) (Kimura et al., 2022; McWilliams et al., 2018; Roberts et al., 2016). Similarly, of 50 missense variants classified as VUSs in ClinVar that also have previously reported functional data, only two variants, p.G35Q and p.G101R, had divergent functional classifications. Both variants were previously reported to have functionally neutral effects but were characterized as functionally deleterious in our assay (Figure 2C, Appendix 1-table 2, 3).

Figure 2.

Comparison to in silico prediction algorithms

As in silico predictions of variant effect are integrated into ACMG variant interpretation guidelines as supporting evidence, we compared the ability of different algorithms, including recently described algorithms that incorporate deep-learning models of protein structure to predict the functional consequence of CDKN2A missense variants. Using our functional classifications for all CDKN2A missense variants as truth, we compared our functional classifications to predictions from CADD, PolyPhen-2, SIFT, VEST, AlphaMissense, ESM1b, and PrimateAI-3D. Predictions for all missense variants (1,182 functionally deleterious and 1,782 functionally neutral) were available for comparison for all algorithms, except CADD and PrimateAI-3D, where 910 (349 functionally deleterious and 561 functionally neutral) and 904 (349 functionally deleterious and 555 functionally neutral) missense variants had predictions available respectively (Appendix 1-table 4). In silico variant effect predictors performed similarly across a broad range of performance characteristics (Appendix 1-table 5). Accuracy of in silico model predictions were 54.6 – 70.9% (CADD – 56.8%; PolyPhen-2 – 54.6%; SIFT – 61.4%; VEST – 70.8%; AlphaMissense – 70.9%; ESM1b – 64.9%; and PrimateAI-3D; 66.7%) (Figure 3). We also assessed sensitivity, specificity, positive predictive value, and negative predictive value for each model. We found that sensitivity was 0.15 – 0.90 (CADD – 0.86; PolyPhen-2 – 0.9; SIFT – 0.64; VEST – 0.67; AlphaMissense – 0.69; ESM1b – 0.77; and PrimateAI-3D – 0.15), specificity was 0.31 – 0.99 (CADD – 0.39; PolyPhen-2 – 0.32; SIFT – 0.60; VEST – 0.74; AlphaMissense – 0.72; ESM1b – 0.57; and PrimateAI-3D – 0.99), positive predictive value was 0.46 – 0.93 (CADD – 0.47; PolyPhen-2 – 0.46; SIFT – 0.51; VEST – 0.63; AlphaMissense – 0.62; ESM1b – 0.54; and PrimateAI-3D – 0.93), and negative predictive value was 0.65 – 0.82 (CADD – 0.81; PolyPhen-2 – 0.82; SIFT – 0.71; VEST – 0.77; AlphaMissense – 0.78; ESM1b – 0.79; and PrimateAI-3D – 0.65).

Figure 3.

Distribution of functionally deleterious variants

Functionally deleterious missense variants were not distributed evenly across CDKN2A. CDKN2A contains four ankyrin repeats that mediate protein-protein interactions, ankyrin repeat 1 at codon 11-40, ankyrin repeat 2 at codon 44-72, ankyrin repeat 3 at codon 77-106, and ankyrin repeat 4 at codon 110-139 (Goldstein, 2004; Ruas and Peters, 1998; Sun et al., 2010) (Figure 2-figure supplement 1A). Functionally deleterious variants were enriched in ankyrin repeat 1 (44.8%, adjusted P value = 8.5 x 10^-4), ankyrin repeat 2 (47.4%, adjusted P value = 1.4 x 10^-6), and ankyrin repeat 3 (55.0%, adjusted P value = 6.1 x 10^-22), and depleted in ankyrin repeat 4 (27.2%, adjusted P value = 1.6 x 10^-8), non-ankyrin repeat residue 1-10 (22.0%, adjusted P value = 1.5 x 10^-5), and residues 140-156 (9.1%, adjusted P value = 4.5 x 10^-30) (Figure 2-figure supplement 1B). Moreover, functionally deleterious variants were further enriched within 10 residue subregions of ankyrin repeats 1-3, with 59.5% of variants in residues 14 – 23 of ankyrin repeat 1, 63.5% of variants in residues 46-55 of ankyrin repeat 2, and 81.5% of variants in residues 80-89 of ankyrin repeat 3 being classified as functionally deleterious (Figure 2D, Appendix 1-table 2). Furthermore, analysis of functionally deleterious variants may highlight critical and non-critical resides for CDKN2A function. Across all single residues, the mean percent of functionally deleterious missense variants was 39.9% (95% confidence interval: 34.7% – 45.0%) (Figure 2-figure supplement 1C). At six amino acid residues, p.G55, p.P81, p.H83, p.D84, p.G89, and p.P114, all missense variants were functionally deleterious. These residues are conserved between human and murine p16 (Byeon et al., 1998). Furthermore, p.H83 has been reported to stabilized peptide loops connecting the helix-turn-helix structure exhibited by four ankyrin repeats (Byeon et al., 1998), whereas p.D84 and p.G89 are located in a 20- residue region reported to interact with CDK4 and CDK6 (Fåhraeus et al., 1996). Conversely, at 18 residues, no missense variant was characterized as functionally deleterious (Appendix 1-table 2).

Functional effect of CDKN2A somatic mutations

Somatic alterations in CDKN2A are a frequent finding in many types of cancer. However, not all somatic alterations are unequivocally deleterious to protein function. Missense somatic mutations, for example, are particularly challenging to functionally interpret and the presence of a functionally neutral somatic mutation may impact patient care (Tung et al., 2020). To understand the functional effect of missense somatic mutations in CDKN2A, we functionally classified mutations reported in the Catalogue Of Somatic Mutations In Cancer (COSMIC) (Forbes et al., 2009), The Cancer Genome Atlas (TCGA) (Muddabhaktuni and Koyyala, 2021), patients with cancer undergoing sequencing at The Johns Hopkins University School of Medicine (JHU), Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets Clinical Sequencing Cohort (MSK-IMPACT) (Cheng et al., 2015). Overall, 355 unique missense somatic mutations were reported, of which 193 (54.4%) were functionally deleterious (Appendix 1-table 6). The percent of missense somatic mutations that were classified as functionally deleterious was greater than the percent of all possible CDKN2A missense variants that were classified as functionally deleterious, suggesting enrichment of functionally deleterious missense changes among somatic mutations (Figure 2A, Appendix 1-table 6). The prevalence of functionally deleterious missense somatic mutations was similar in COSMIC, TCGA, JHU, and MSK-IMPACT with 55.9%, 68.0%, 65.7%, and 66.7% of mutations being classified as functionally deleterious, respectively (Figure 4A, Appendix 1-table 6). Similar to all functionally deleterious variants, functionally deleterious missense somatic mutations were not distributed evenly across CDKN2A. Functionally deleterious somatic mutations were enriched within the ankyrin repeat 3 (Figure 4B, Appendix 1-table 6), We found that 29.0%, 37.1%, 50.8%, and 42.9% of all functionally deleterious missense somatic mutations occurred within ankyrin repeat 3 in COSMIC, TCGA, JHU, and MSK-IMPACT, respectively, and within this domain, 62.7%, 73.1%, 72.7%, and 70.8% of functionally deleterious mutations were in residues 80-89 in COSMIC, TCGA, JHU, and MSK-IMPACT, respectively (Figure 4B).

Figure 4.

We were also able to assess the contribution of functionally deleterious CDKN2A missense somatic mutations in COSMIC, TCGA, JHU, and MSK-IMAPCT by cancer type, and found that 44.4 – 95.7% of reported CDKN2A missense somatic mutations were functionally deleterious when stratified by cancer type (Figure 4-figure supplement 1A-D). When considering missense somatic mutation reported in all databases, there was enrichment of functionally deleterious mutations in melanoma (84.8%; adjusted P value – 0.019) and depletion of functionally deleterious mutations in colorectal adenocarcinoma (49.0%; adjusted P value = 2.6 x 10^-4) (Figure 4-figure supplement 2). As the proportion of missense somatic mutations that were functionally deleterious was less in colorectal carcinoma compared to other types of cancer, we assessed whether somatic mutations in mismatch repair genes (MLH1, MSH2, MSH6, and PMS2) were associated with the functional status of CDKN2A missense somatic mutations. Thirty-six samples in COSMIC had a CDKN2A missense somatic mutation, of which 12 samples (33.3%) had a somatic mutation in a mismatch repair gene. We found that 3 of 12 samples (25%) with a somatic mutation in a mismatch repair gene had a functionally deleterious CDKN2A missense somatic mutation compared to 12 of 24 samples (50%) without a somatic mutation in a mismatch repair gene (Fisher’s exact test; P = 0.2821).

Discussion

VUSs in hereditary cancer susceptibility genes, predominantly rare missense variants, are a frequent finding in patients undergoing genetic testing and the cause of significant uncertainty. ACMG variant interpretation guidelines incorporate functional data, as well as other evidence such as in silico predictions of variant effect, to aid classification of variants as either pathogenic or benign. CDKN2A VUSs are a frequent finding in patients with PDAC. We previously found that over 40% of CDKN2A VUSs were functionally deleterious and therefore could be reclassified as likely pathogenic using ACMG variant interpretation guidelines. In this study, we developed a validated high-throughput in vitro assay and functionally characterized 2,964 CDKN2A missense variants, representing all possible single amino acid variants. We found that 1,182 missense variants (39.9%) were functionally deleterious. These pre-defined functional characterizations are resource for the scientific community and can be integrated into variant interpretation schema, such as those from the ACMG, to aid classification of CDKN2A germline variants and somatic mutations.

Our comprehensive characterization of all possible CDKN2A missense variants allowed us to assess the ability of in silico algorithms – including recently published predictors based on machine learning AlphaMissense, ESM1b, and PrimateAI-3D – to predict the pathogenicity or functional effect of CDKN2A missense variants. We found that all in silico variant effect predictors assessed performed similarly. Overall, PolyPhen-2 had highest sensitivity and negative predictive value at 0.90 and 0.82 respectively. PrimateAI-3D had the highest specificity and positive predictive values at 0.99 and 0.93, respectively. However, while specificity and positive predictive values were high, Primate AI-3D lowest sensitivity and negative predictive values at 0.15 and 0.65 respectively. Highest accuracy was observed with AlphaMissense at 70.9%, closely followed by VEST at 70.8%. Given that reclassification of VUSs in hereditary cancer genes into inappropriate strata has significant implications for patients, integration of in silico models, including those utilizing machine learning, may be premature. Ultimately, our data support current ACMG guidelines that include in silico predictions of variant effect as supporting evidence of pathogenicity or benign impact.

Our study also provides other insight for the implementation of variant interpretation guidelines. ACMG guidelines include presence of a missense variants at a residue with a previously reported pathogenic variant as moderate evidence of pathogenicity. We found that the mean percent of functionally deleterious missense variants per residue was 39.9% and that only six amino acid residues were all missense variants functionally deleterious. These data suggest, at least for CDKN2A, that the presence of a pathogenic missense variant at a residue should be used with caution when classifying other missense variants at the same residue.

Our high-throughput functional assay characterized variants based upon a broad cellular phenotype, cell proliferation, in a single PDAC cell line. However, there appear to be limited cell-specific and assay-specific differences in functional classifications of CDKN2A variants. In our previous study, we characterized 29 CDKN2A VUSs in three PDAC cell lines, using cell proliferation and cell cycle assays, and found agreement between all functional classifications (Kimura et al., 2022). Nevertheless, our assay may not encompass all cellular functions of CDKN2A and underestimate the number of functionally deleterious missense variants. Moreover, in this study, we found that functionally deleterious effects were enriched among somatic missense mutations compared to all CDKN2A missense variants, further supporting the veracity of our functional classifications. Furthermore, in silico models to predict variant effect may characterize function (CADD, PolyPhen-2, and SIFT) or pathogenicity (VEST, AlphaMissense, ESM1b, and PrimateAI-3D). As our assay does not directly characterize pathogenicity, to compare in silico assays, we assumed that predicted pathogenic variants were functionally deleterious.

In this study, we determined functional classifications for all possible CDKN2A missense variants to aid variant classifications. Furthermore, comparison of our functional classifications to in silico variant effect predictors, including recently described algorithms based on machine learning, provides performance benchmarks and supports current recommendations integrating data computational data into variant interpretation guidelines.

Methods

Cell lines

PANC-1 (American Type Culture Collection, Manassas, VA; catalog no. CRL-1469), a human PDAC cell line with a homozygous deletion of CDKN2A (Caldasl et al., 1994) and 293T (American Type Culture Collection; catalog no. CRL-3216), a human embryonic kidney cell line, were maintained in Dulbecco’s modified Eagle’s medium (Thermo Fisher Scientific Inc., Waltham, MA; catalog no.11995-065) supplemented with 10% fetal bovine serum (Thermo Fisher Scientific Inc.; catalog no. 26140-079). Cell line authentication and mycoplasma testing were performed using the GenePrint 10 System (Promega Corporation, Madison, WI; catalog no. B9510) and the PCR-based MycoDtect kit (Greiner Bio-One, Monroe, NC; catalog no. 463 060) (Genetics Resource Core Facility, The Johns Hopkins University, Baltimore, MD).

CDKN2A somatic mutation data

CDKN2A (p16^INK4; NP_000068.1) missense somatic mutation data was obtained from the Catalogue Of Somatic Mutations In Cancer (Forbes et al., 2009), The Cancer Genome Atlas (Muddabhaktuni and Koyyala, 2021), patients with cancer undergoing sequencing at The Johns Hopkins University School of Medicine (Baltimore, MD), Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets Clinical Sequencing Cohort (Cheng et al., 2015).

Plasmids

pHAGE-CDKN2A (Addgene, Watertown, MA; plasmid no. 116726) was created by Gordon Mills & Kenneth Scott (Ng et al., 2018). pLJM1 (Addgene; plasmid no. 91980) was created by Joshua Mendell (Golden et al., 2017). pLentiV_Blast (Addgene, plasmid no. 111887) was created by Christopher Vakoc (Tarumoto et al., 2020). psPAX2 (Addgene, plasmid no. 12260) was created by Didier Trono), and pCMV-VSV-G (Addgene, plasmid no. 8454) was created by Bob Weinberg (Stewart et al., 2003).

CDKN2A expression plasmid libraries

CDKN2A cDNA from pHAGE-CDKN2A was subcloned into the pLJM1 plasmid as previously described (Kimura et al., 2022). Codon-optimized CDKN2A cDNA using p16^INK4A amino acid sequence (NP_000068.1), was designed (Appendix 1-table 1) and pLJM1 containing codon optimized CDKN2A (pLJM1-CDKN2A) generated (Twist Bioscience, South San Francisco, CA). Then, 156 plasmid libraries were synthesized using pLJM1-CDKN2A such that each library contained all possible 20 amino acids variants (19 missense and 1 synonymous) at a given position.

Single variant CDKN2A expression plasmids

Individual pLJM1-CDKN2A expression constructs for CDKN2A missense variants, p.L32L, p.L32P, p.G101G, p.G101W, p.V126D, and p.V126V were generated using the Q5 Site-Directed Mutagenesis kit (New England Biolabs, Ipswich, MA; catalog no. E0552). Primers used for site-directed mutagenesis are given in Appendix 1-table 7. Integration of each CDKN2A variant was confirmed using Sanger sequencing (Genewiz, Plainsfield, NJ) using the CMV Forward sequencing primer (CGCAAATGGGCGGTAGGCGTG). The manufacturer’s protocol was followed unless otherwise specified.

CellTag plasmid library

Twenty nonfunctional 9 base pair barcodes “CellTags” were subcloned into pLentiV_Blast using Q5® Site-Directed Mutagenesis kit (New England Biolabs; catalog no. E0552) (Biddy et al., 2018). Primers used to generate each CellTag plasmid are given in Appendix 1-table 7. Integration of each CellTag was confirmed using Sanger sequencing (Genewiz) (sequencing primer: AACTGGGAAAGTGATGTCGTG). The manufacturer’s protocol was followed unless otherwise specified. CellTag plasmids were then pooled to form a CellTag plasmid library with equal representation of each CellTag plasmid.

Lentivirus production

Lentivirus production was performed as previously described with the following modifications (Kimura et al., 2022). pLJM1 lentiviral expression vectors (plasmid libraries and single variant expression plasmids) and lentiviral packaging vectors (psPAX2 and pCMV-VSV-G) were transfected into 293T cells using Lipofectamine 3000 Transfection Reagent (Thermo Fisher Scientific, Waltham, MA; catalog no. L3000008). Media was collected at 24 hours and 48 hours, pooled, and lentiviral particles concentrated using Lenti-X Concentrator (Clontech, Mountain View, CA; catalog no. 631231).

Lentiviral transduction

PANC-1 cells were used for CDKN2A plasmid library and single variant CDKN2A expression plasmid transductions. PANC-1 cells previously transduced with pLJM1-CDKN2A (PANC- 1^CDKN2A) were used for CellTag library transductions. Briefly, 1 x 10⁵ cells were cultured in media supplemented with 10 ug/ml polybrene and transduced with x 10⁷ transducing units per mL of lentivirus particles. Cells were then centrifuged at 1,200 x g for 1 hour. After 48 hours of culture at 37°C and 5% CO₂, transduced cells were selected using 3 µg/ml puromycin (CDKN2A plasmid libraries and single variant CDKN2A expression plasmids) or 5 µg/ml blasticidin (CellTag plasmid library) for 7 days. After selection, cells were trypsinized for passage into T150 flasks and DNA collection. T150 flasks were cultured for 2-5 weeks until confluent and then DNA collected. DNA was extracted from PANC-1 cells using the PureLink^TM Genomic DNA Mini Kit (Invitrogen, Carlsbad, CA; catalog no. K1820-01).

Generation of sequence libraries

Library preparation and sequencing was performed as previously described with the following modifications (Kinde et al., 2011). For the 1^st stage PCR, 3 target specific primers were designed to amplify CDKN2A amino acid positions 1 to 53, 54 to 110, and 111 to 156. Forward and reverse 1^st stage primers contained 5’ sequence, M13F (GTAAAACGACGGCCAGC) and M13R (CAGGAAACAGCTATGAC) respectively, to enable second stage of amplification and ligation of Illumina adapter sequences (Appendix 1-table 7). DNA was amplified with Q5 Hot Start High-Fidelity 2X Master Mix (New England Biolabs; catalog no. M0494S). For the 1^st stage PCR, each DNA sample was amplified in three reactions each containing 66 ng of DNA.1^st stage PCR products for each sample were then pooled and purified using the Agencourt AMPure XP system (Beckman Coulter, Inc, Brea, CA; catalog no. A63881) into 50 µL of elution buffer. Purified PCR product was amplified in a 2^nd stage PCR to add Illumina adaptor sequences and indexes (Appendix 1-table 7). PCR Amplification was performed with KAPA HiFi HotStart PCR Kit (Kapa Biosystems, Wilmington, MA; catalog no. KK2501) in 25 µL reactions containing 5X KAPA HiFi Buffer - 5 µL, 10 mM KAPA dNTP Mix - 0.75 µL, 10 μM forward primer - 0.75 µL, 10 μM reverse primer - 0.75 µL. For the 1^st stage PCR, 66 ng of template DNA and 12.5 µL, Q5 Hot Start High-Fidelity 2X Master Mix was used with the following cycling conditions: 98 °C for 30 seconds; 25 cycles of 98 °C for 10 seconds, 72 °C for 30 seconds, 72 °C for 25 seconds; 72 °C for 2 minutes. For the 2^nd stage PCR, 0.25 µL of of 1^st stage PCR product and 0.5 µL of 1 U/μL KAPA HiFi HotStart DNA Polymerase was used with the following cycling conditions: 95 °C for 3 minutes; 25 cycles of 98 °C for 20 seconds, 62 °C for 15 seconds, 72 °C for 1 minute. 2^nd stage PCR products were purified with the Agencourt AMPure XP system (Beckman Coulter, Inc.; catalog no. A63881) into 30 µL of elution buffer. Samples were quantified by Qubit using dsDNA HS assay kit (Invitrogen; catalog no. Q33230).

Sequencing and analysis

Sequence libraries were pooled into groups of 16 samples and sequenced on the Illumina MiSeq System (Illumina, San Diego, CA) with the MiSeq Reagent Kit v2 (300 cycles) (Illumina catalog no. MS-102-2002) to generate 150 base pair paired-end reads. Samples were demultiplexed and FASTQ sequenced read files were generated with MiSeq control software 2.5.0.5 (Illumina). Paired sequence reads were then combined into a single contiguous sequence using Paired-End Read Merger (Zhang et al., 2014). Reads supporting each variant at a given amino acid position were counted using perl.

Functional characterization of CDKN2A variants using gamma generalized linear model

We determined if a variant has a fitness advantage by assessing the significance of the observed ratio r_v,cf at confluence between the number of cells with a missense variant 𝑣 and the number of cells with a synonymous variant at a given amino acid position. Under the assumption that the missense variant is neutral (null model), we assumed that the distribution of r_v,cf can be explained by two key covariates: 𝑟_{𝑣,𝑖𝑛𝑖𝑡}, which represent the missense variant-to-synonymous variant ratio at day 9, and 𝑝_{𝑣,𝑖𝑛𝑖𝑡}, the proportion of the missense variant cells among other variants, including the synonymous variant, at the studied position. More specifically, given the variables 𝑟_{𝑣,𝑖𝑛𝑖𝑡} 𝑎𝑛𝑑 𝑝_{𝑣,𝑖𝑛𝑖𝑡}, the ratio at confluence follows a distribution:

where the mean 𝑢_𝑣 of the Gamma distribution is such that:

Here, the parameters of the null model to estimate are α, 𝑎, 𝑎𝑛𝑑 𝑏, where α, is the shape parameter of the Gamma distribution and is assumed to be the same for all variants. This model is a Gamma Generalized Linear Model (GLM) over the response variable 𝑟_{𝑣,𝑐𝑓} with a log-link function and covariates 𝑙𝑜𝑔(𝑟_{𝑣,𝑖𝑛𝑖𝑡}) and 𝑙𝑜𝑔(𝑝_{𝑣,𝑖𝑛𝑖𝑡}). Estimating the parameters will provide a null distribution of 𝑟_{𝑣,𝑐𝑓}, generating a p-value for every observed 𝑟_{𝑣,𝑐𝑓} for any variant at a given position.

To estimate the parameters α, a, and 𝑏, we utilized three control experiments where the CellTag plasmid library was transduced into PANC-1^CDKN2Aco cells so that each CellTag represented a neutral variant. For a single experiment, every variant can be considered as wild-type, and we test the other 19 variants against it, knowing that they are neutral and therefore follow the null distribution. This provides us with 19 x 20 triplets , for every experiment, yielding 1140 datapoints when considering all three experiments together. To estimate the parameters using these 1140 data points, we fit the GLM corresponding GLM model using the sklearn.linear_model module.

After the estimation of parameters α, a, and 𝑏, every observation for a tested variant 𝑣 at a given position of the triplet yields a p-value, defined as the probability of observing a ratio at confluence that is at least 𝑟_{𝑣,𝑐𝑓} given 𝑝_{𝑣,𝑖𝑛𝑖𝑡}, 𝑟_{𝑣,𝑖𝑛𝑖𝑡} under the null Gamma model. As some variants were tested in repeated experiments, we combined their associated p-values into a single p-value using Fisher’s method. Finally, to determine if a variant presents a fitness advantage, we apply a Benjamini-Hochberg estimator on all the tested variants p-values, fixing the False Discovery Rate at a level of 0.05.

Data visualization

Heat map of individual variant p-values by amino position was generated using R with the heatmaply package (Galili et al., 2018).

Cell proliferation assay

Cell proliferation assay were performed as previously described with the following modifications (Kimura et al., 2022). 1 × 10⁵ cells were seeded into in vitro culture on day 0. Cell were counted on day 14 using a TC20 Automated Cell Counter (Bio-Rad Laboratories, Herclues, CA; catalog no. 1450102). Relative cell proliferation value was calculated as cell number normalized to empty vector control. Assays were repeated in triplicate. Mean cell proliferation value and standard deviation (s.d.) were calculated.

Variant effect predictions

Publicly available algorithms were used to predict the consequence of CDKN2A missense variants. Prediction algorithms used included: Combined Annotation Dependent Depletion (CADD) (Kircher et al., 2014), Polymorphism Phenotyping v2 (PolyPhen-2) (Adzhubei et al., 2010), Sorting Intolerant From Tolerant (SIFT) (Kumar et al., 2009), Variant Effect Scoring Tool score (VEST) (Carter et al., 2013), AlphaMissense (18), ESM1b (Brandes et al., 2023), and PrimateAI-3D (Gao et al., 2023) (Appendix 1-table 4). PolyPhen-2, SIFT, VEST, AlphaMissense, and ESM1b prediction were available for all missense variants. CADD scores were available for 910 missense variants and where multiple CADD scores were possible, mean values were used. PrimateAI-3D prediction scores were available for 904 assayed missense variants.

Statistical analyses

Statistical analyses were performed using JMP v.11 (SAS, Cary, NC) and Python statsmodel package (version 0.14.0). Cell proliferation value means were compared with the Student’s t-test. Proportions of functionally deleterious missense variants and somatic mutations were compared with the Z-test and multiple test correction performed with the Bonferroni method. P values < 0.05 were considered statistically significant.

Acknowledgements

Funding

National Institutes of Health grant P50CA62924 (NJR) Susan Wojcicki and Dennis Troper (NR)

The Sol Goldman Pancreatic Cancer Research Center (NJR) The Rolfe Pancreatic Cancer Foundation (NJR)

The Japanese Society of Gastroenterology Support for Young Gastroenterologists Studying in the United States (HK)

The Japan Society for the Promotion of Science Overseas Research Fellowships (HK)

Author contributions

Conceptualization: HK, KL, CT, NJR Resources: CT, NJR

Data curation: HK, KL, CT, NJR Formal analysis: HK, KL, CT, NJR Investigation: HK, KL, CT, NJR Visualization: HK

Methodology: HK, KL, CT, NJR Writing-original draft: HK, NJR

Project administration: NJR

Writing-review and editing: HK, KL, CT, NJR

Competing interests

Authors declare that they have no competing interests.

Data and materials availability

All data are available in the main text or the supplementary materials.

Supplementary Information

Figures

Figure 1-figure supplement 1. Development and validation of high-throughput CDKN2A functional assay.

Figure 2-figure supplement 1. Functional characterization of all possible CDKN2A missense variants.

Figure 4-figure supplement 1. Functional classification of missense somatic mutations in CDKN2A.

Figure 4-figure supplement 2. Aggregate functional classifications for missense somatic mutations in CDKN2A

Tables

Appendix 1-table 1. Codon optimized CDKN2A sequence.

Appendix 1-table 2. Assay outputs and functional classifications for all possible CDKN2A missense and synonymous variants.

Appendix 1-table 3. Benchmark pathogenic variants, benchmark benign variants, and variants of uncertain significance with previously reported function data.

Appendix 1-table 4. In silico variant effect predictions for CDKN2A missense variants. Appendix 1-table 5. Assessment of in silico variant effect prediction models.

Appendix 1-table 6. Missense somatic mutations in CDKN2A reported in COSMIC, TCGA, JHU, MSK-IMPACT.

Appendix 1-table 7. Sequences of primers used in study.

Source data

Figure 1-source data Raw data in Figure 1

Figure 1-figure supplement 1-source data 1 Raw data in Figure 1-figure supplement 1A Figure 1-figure supplement 1-source data 2 Raw data in Figure 1-figure supplement 1B Figure 2-source data 1

Raw data in Figure 2A Figure 2-source data 2

Raw data in Figure 2B and C Figure 2-source data 3

Raw data in Figure 2D

Figure 2-figure supplement 1-source data 1

Raw data in Figure 2-figure supplement 1B

Figure 2- figure supplement 1-source data 2

Raw data in Figure 2- figure supplement 1C

Figure 3-source data 1

Raw data in Figure 3

Figure 4-source data 1

Raw data in Figure 4

Figure 4-figure supplement 1-source data 1

Raw data in Figure 4-figure supplement 1

Figure 4-figure supplement 2-source data 1

Raw data in Figure 4-figure supplement 2

Figure 1-figure supplement 1.

Figure 2-figure supplement 1.

Figure 4-figure supplement 1.

Figure 4-figure supplement 2.

Appendix 1-table 1.

Appendix 1-table 5.