Patterns of HER2 expression and genomic correlates in lung cancer, with a focus on preanalytical variables impacting immunohistochemical staining results.

Aims: Fam-trastuzumab deruxtecan-nxki (T-DXd) was recently approved for advanced stage or metastatic solid tumours with human epidermal growth factor receptor 2 (HER2) immunohistochemical (IHC) 3+ staining. Data on HER2 IHC testing and knowledge of genomic correlates in lung cancer are scarce. This study analyses genomic characteristics of HER2-expressing tumours and addresses issues with preanalytical variables for lung cancer specimens.

Identifying somatic fingerprints of cancers defined by germline and environmental risk factors.

Numerous studies over the past generation have identified germline variants that increase specific cancer risks. Simultaneously, a revolution in sequencing technology has permitted high-throughput annotations of somatic genomes characterizing individual tumors. However, examining the relationship between germline variants and somatic alteration patterns is hugely challenged by the large numbers of variants in a typical tumor, the rarity of most individual variants, and the heterogeneity of tumor somatic fingerprints.

Topical hidden genome: discovering latent cancer mutational topics using a Bayesian multilevel context-learning approach.

Inferring the cancer-type specificities of ultra-rare, genome-wide somatic mutations is an open problem. Traditional statistical methods cannot handle such data due to their ultra-high dimensionality and extreme data sparsity. To harness information in rare mutations, we have recently proposed a formal multilevel multilogistic "hidden genome" model.

Predicting Cancer Risk from Germline Whole-exome Sequencing Data Using a Novel Context-based Variant Aggregation Approach.

Unlabelled: Many studies have shown that the distributions of the genomic, nucleotide, and epigenetic contexts of somatic variants in tumors are informative of cancer etiology. Recently, a new direction of research has focused on extracting signals from the contexts of germline variants and evidence has emerged that patterns defined by these factors are associated with oncogenic pathways, histologic subtypes, and prognosis. It remains an open question whether aggregating germline variants using meta-features capturing their genomic, nucleotide, and epigenetic contexts can improve cancer risk prediction.

Combining Breast Cancer Risk Prediction Models.

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.

Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes.

(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.

Exome-Wide Pan-Cancer Analysis of Germline Variants in 8,719 Individuals Finds Little Evidence of Rare Variant Associations.

Background: Many cancer types show considerable heritability, and extensive research has been done to identify germline susceptibility variants. Linkage studies have discovered many rare high-risk variants, and genome-wide association studies (GWAS) have discovered many common low-risk variants. However, it is believed that a considerable proportion of the heritability of cancer remains unexplained by known susceptibility variants.

Mining mutation contexts across the cancer genome to map tumor site of origin.

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.

Familial pancreatic cancer: who should be considered for genetic testing?

Background: Determining how many female patients who underwent breast imaging meet the eligibility criteria for genetic testing for familial pancreatic cancer (FPC).

Methods: A total of 42,904 patients seen at the Newton-Wellesley Hospital between 2007 and 2009 were retrospectively reviewed. The first four categories were based on pancreatic cancer-associated syndromes: (1) hereditary breast and ovarian cancer (HBOC), (2) Lynch syndrome (LS), (3) familial atypical multiple mole melanoma (FAMMM), and (4) family history of FPC (FH-FPC).

Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort.

Background: Several breast cancer risk-assessment models exist. Few studies have evaluated predictive accuracy of multiple models in large screening populations.

Methods: We evaluated the performance of the BRCAPRO, Gail, Claus, Breast Cancer Surveillance Consortium (BCSC), and Tyrer-Cuzick models in predicting risk of breast cancer over 6 years among 35 921 women aged 40-84 years who underwent mammography screening at Newton-Wellesley Hospital from 2007 to 2009.

Performance of Screening Breast MRI across Women with Different Elevated Breast Cancer Risk Indications.

Background Screening breast MRI is recommended for women with mutation or a history of chest radiation, but guidelines are equivocal for MRI screening of women with a personal history of breast cancer or high-risk lesion. Purpose To evaluate screening breast MRI performance across women with different elevated breast cancer risk indications. Materials and Methods All screening breast MRI examinations performed between 2011 and 2014 underwent retrospective medical record review.