Purpose: The incidence of human papillomavirus (HPV)–associated head and neck squamous cell carcinoma (HPV + HNSCC) is increasing in the United States. Currently, there are no early detection approaches for HPV + HNSCC. Two blood-based analytes for early detection and diagnosis of HPV + HNSCC, circulating tumor HPV DNA (ctHPVDNA) and HPV early protein antibodies (HPV Ab), show promise, yet current approaches lack adequate diagnostic accuracy for broad clinical utility. Further, performance metrics across various assays for detecting these analytes alone or in combination have not been compared head-to-head. To address these limitations and knowledge gaps, we developed a multifeature HPV whole-genome sequencing (WGS) liquid biopsy for improved low-level ctHPVDNA detection. We defined the performance characteristics of this WGS-based approach and compared it head-to-head with existing blood-based HPV detection approaches to determine the optimal single or combinatorial biomarker strategy for a future prospective study of HPV + HNSCC early detection. Experimental Design: We tested blood samples from 304 participants: 152 patients with untreated incident HPV + HNSCC (77% stage I) and 152 general population control patients. We compared WGS-based ctHPVDNA detection, single-plex Droplet Digital PCR (ddPCR)–based ctHPVDNA detection, multiplex ddPCR-based ctHPVDNA detection, multiplex HPV Ab detection, and clinical standard-of-care tissue biopsy, benchmarked to gold-standard HPV + HNSCC tissue diagnosis. We then modeled the operational feasibility of these approaches as screening biomarkers for HPV + HNSCC. Results: HPV WGS sensitivity and specificity were 98.7% and 98.7%, respectively. Single-plex ddPCR sensitivity and specificity were 94.2% and 98.6%, respectively. Multiplex ddPCR sensitivity and specificity were 90.6% and 96.3%, respectively. HPV Ab sensitivity and specificity were 86.4% and 96.3%, respectively. A combinatorial approach using both HPV WGS and HPV Ab yielded a sensitivity and specificity of 87.4% and 98.8%, respectively. In a head-to-head comparison, HPV WGS demonstrated significantly improved diagnostic accuracy compared with ddPCR (Youden index for HPV WGS, 0.99 vs. ddPCR, 0.90; P < 0.001), HPV Ab (HPV WGS, 0.99 vs. HPV Ab, 0.83; P < 0.001), and clinical workup (HPV WGS, 0.99 vs. clinical workup, 0.82; P < 0.001), which was maintained when evaluating only early-stage disease cases. For men ages 55 to 74, HPV WGS yielded the lowest number needed to screen (2,903 men) and the highest positive predictive value (2.6). Conclusions: HPV WGS–based ctHPVDNA detection demonstrated the highest sensitivity, specificity, and diagnostic accuracy and thus the lowest number needed to screen and highest positive predictive value compared with ddPCR-based ctHPVDNA detection, HPV Ab–based detection, and combinatorial approaches. These results highlight the promise of HPV WGS liquid biopsy for screening and early detection of HPV + HNSCC and the need for modeling and cost-effectiveness studies to evaluate and guide screening implementation.

(1) Background: The purpose of this study is to compare the performance of four breast cancer risk prediction models by race, molecular subtype, family history of breast cancer, age, and BMI. (2) Methods: Using a cohort of women aged 40–84 without prior history of breast cancer who underwent screening mammography from 2006 to 2015, we generated breast cancer risk estimates using the Breast Cancer Risk Assessment tool (BCRAT), BRCAPRO, Breast Cancer Surveillance Consortium (BCSC) and combined BRCAPRO+BCRAT models. Model calibration and discrimination were compared using observed-to-expected ratios (O/E) and the area under the receiver operator curve (AUC) among patients with at least five years of follow-up. (3) Results: We observed comparable discrimination and calibration across models. There was no significant difference in model performance between Black and White women. Model discrimination was poorer for HER2+ and triple-negative subtypes compared with ER/PR+HER2−. The BRCAPRO+BCRAT model displayed improved calibration and discrimination compared to BRCAPRO among women with a family history of breast cancer. Across models, discriminatory accuracy was greater among obese than non-obese women. When defining high risk as a 5-year risk of 1.67% or greater, models demonstrated discordance in 2.9% to 19.7% of patients. (4) Conclusions: Our results can inform the implementation of risk assessment and risk-based screening among women undergoing screening mammography.

The vast preponderance of somatic mutations in a typical cancer are either extremely rare or have never been previously recorded in available databases that track somatic mutations. These constitute a hidden genome that contrasts the relatively small number of mutations that occur frequently, the properties of which have been studied in depth. Here we demonstrate that this hidden genome contains much more accurate information than common mutations for the purpose of identifying the site of origin of primary cancers in settings where this is unknown. We accomplish this using a projection-based statistical method that achieves a highly effective signal condensation, by leveraging DNA sequence and epigenetic contexts using a set of meta-features that embody the mutation contexts of rare variants throughout the genome.

Background: Early detection of HPV-associated oropharyngeal squamous cell carcinoma, the most common HPV cancer in the United States, could reduce disease-related morbidity and mortality, yet currently, there are no early detection tests. HPV circulating tumor DNA (ctDNA) is a sensitive and specific biomarker for HPV–associated oropharyngeal squamous cell carcinoma at diagnosis. It is unknown if ctDNA HPV is detectable prior to diagnosis, and thus its potential as an early detection test is also unknown. Methods: Plasma samples from the Mass General Brigham Biobank collected 1.3-10.8 years prior to diagnosis from HPV–associated oropharyngeal squamous cell carcinoma patients (n = 28) and age- and sex-matched controls (n = 28) were blinded and run on a newly developed and validated multifeature HPV whole genome sequencing liquid biopsy assay and a validated HPV antibody assay. Results: HPV ctDNA results were positive in 22 of 28 prediagnostic samples from HPV–associated oropharyngeal squamous cell carcinoma cases (sensitivity 79%) with a maximum lead time of 7.8 years. HPV ctDNA results were negative in all controls (0 of 28, 100% specificity). Diagnostic accuracy was highest within 4 years of cancer diagnosis and was higher than HPV Ab detection within the same timeframe (P = .004). Application of a machine-learning model trained and tested on an independent cohort of 306 cases and controls increased the sensitivity of detection to 27 of 28 cases (overall sensitivity 96%) and the maximum lead time to 10.3 years. Conclusions: HPV ctDNA can be detected in the blood years prior to diagnosis with HPV–associated oropharyngeal squamous cell carcinoma, with high specificity, in a case-control cohort of 56 participants. HPV ctDNA detection alone, or in combination with previously identified serological biomarkers, may be a feasible approach to early detection of HPV–associated oropharyngeal squamous cell carcinoma.

Accurate risk stratification is key to reducing cancer morbidity through targeted screening and preventative interventions. Multiple breast cancer risk prediction models are used in clinical practice, and often provide a range of different predictions for the same patient. Integrating information from different models may improve the accuracy of predictions, which would be valuable for both clinicians and patients. BRCAPRO is a widely used model that predicts breast cancer risk based on detailed family history information. A major limitation of this model is that it does not consider non-genetic risk factors. To address this limitation, we expand BRCAPRO by combining it with another popular existing model, BCRAT (i.e., Gail), which uses a largely complementary set of risk factors, most of them non-genetic. We consider two approaches for combining BRCAPRO and BCRAT: (1) modifying the penetrance (age-specific probability of developing cancer given genotype) functions in BRCAPRO using relative hazard estimates from BCRAT, and (2) training an ensemble model that takes BRCAPRO and BCRAT predictions as input. Using both simulated data and data from Newton-Wellesley Hospital and the Cancer Genetics Network, we show that the combination models are able to achieve performance gains over both BRCAPRO and BCRAT. In the Cancer Genetics Network cohort, we show that the proposed BRCAPRO + BCRAT penetrance modification model performs comparably to IBIS, an existing model that combines detailed family history with non-genetic risk factors.

Family history is a major risk factor for many types of cancer. Mendelian risk prediction models translate family histories into cancer risk predictions, based on knowledge of cancer susceptibility genes. These models are widely used in clinical practice to help identify high-risk individuals. Mendelian models leverage the entire family history, but they rely on many assumptions about cancer susceptibility genes that are either unrealistic or challenging to validate, due to low mutation prevalence. Training more flexible models, such as neural networks, on large databases of pedigrees can potentially lead to accuracy gains. In this paper we develop a framework to apply neural networks to family history data and investigate their ability to learn inherited susceptibility to cancer. While there is an extensive literature on neural networks and their state-of-the-art performance in many tasks, there is little work applying them to family history data. We propose adaptations of fully-connected neural networks and convolutional neural networks to pedigrees. In data simulated under Mendelian inheritance, we demonstrate that our proposed neural network models are able to achieve nearly optimal prediction performance. Moreover, when the observed family history includes misreported cancer diagnoses, neural networks are able to outperform the Mendelian BRCAPRO model embedding the correct inheritance laws. Using a large dataset of over 200,000 family histories, the Risk Service cohort, we train prediction models for future risk of breast cancer. We validate the models using data from the Cancer Genetics Network.