TP53_PROF: a machine learning model to predict impact of missense mutations in TP53.

Correctly identifying the true driver mutations in a patient's tumor is a major challenge in precision oncology. Most efforts address frequent mutations, leaving medium- and low-frequency variants mostly unaddressed. For TP53, this identification is crucial for both somatic and germline mutations, with the latter associated with the Li-Fraumeni syndrome (LFS), a multiorgan cancer predisposition. We present TP53_PROF (prediction of functionality), a gene specific machine learning model to predict the functional consequences of every possible missense mutation in TP53, integrating human cell- and yeast-based functional assays scores along with computational scores. Variants were labeled for the training set using well-defined criteria of prevalence in four cancer genomics databases. The model's predictions provided accuracy of 96.5%. They were validated experimentally, and were compared to population data, LFS datasets, ClinVar annotations and to TCGA survival data. Very high accuracy was shown through all methods of validation. TP53_PROF allows accurate classification of TP53 missense mutations applicable for clinical practice. Our gene specific approach integrated machine learning, highly reliable features and biological knowledge, to create an unprecedented, thoroughly validated and clinically oriented classification model. This approach currently addresses TP53 mutations and will be applied in the future to other important cancer genes.

Distinct breast cancer phenotypes in BRCA 1/2 carriers based on ER status.

PURPOSE: BRCA1/2 genes are the two main genes associated with hereditary breast cancers (BC). In the present study, we explore clinical and molecular characteristics of BRCA-associated BC in relation to estrogen receptor (ER) status. METHODS: Three BC databases (DB) were evaluated: (i) Hadassah oncogenetics (n = 4826); (ii) METABRIC (n = 1980), and (iii) Nick-Zainal (n = 560). We evaluated age at diagnosis in BRCA positive (BP) and BRCA negative (BN) patients, and tested for mutational signature differences in cohort iii. mRNA differential expression analysis (DEA) and pathway analysis were performed in cohort ii. RESULTS: Age at diagnosis was lower in BP vs. BN tumors in all cohorts in the ER- group, and only in cohort i for the ER + group. Signature 3 was universal in BP BC, whereas several signatures were associated with ER status. Pathway analysis was performed between BP&BN, and was significant only in ER- tumors: the major activated pathways involved cancer-related processes and were highly significant. The most significant pathway was estrogen-mediated S-phase entry and the most activated upstream regulator was ERBB2. CONCLUSION: Signature 3 was universal for all BP BC, while other signatures were associated with ER status. ER + BP& BN show similar genomic characteristics, ER- BP differed markedly from BN. This suggests that the initial carcinogenic process is universal for all BRCA carriers, but further insults lead to the development of two genomically distinct subtypes ER- and ER + . This may shed light on possible mechanisms involved in BP and carry preventive and therapeutic implications.

High prevalence of cancer-associated TP53 variants in the gnomAD database: A word of caution concerning the use of variant filtering.

The 1,000 genome project, the Exome Aggregation Consortium (ExAC) or the Genome Aggregation database (gnomAD) datasets, were developed to provide large-scale reference data of genetic variations for various populations to filter out common benign variants and identify rare variants of clinical importance based on their frequency in the human population. Using a TP53 repository of 80,000 cancer variants, as well as TP53 variants from multiple cancer genome projects, we have defined a set of certified oncogenic TP53 variants. This specific set has been independently validated by functional and in silico predictive analysis. Here we show that a significant number of these variants are included in gnomAD and ExAC. Most of them correspond to TP53 hotspot variants occurring as somatic and germline events in human cancer. Similarly, disease-associated variants for five other tumor suppressor genes, including BRCA1, BRCA2, APC, PTEN, and MLH1, have also been identified. This study demonstrates that germline TP53 variants in the human population are more frequent than previously thought. Furthermore, population databases such as gnomAD or ExAC must be used with caution and need to be annotated for the presence of oncogenic variants to improve their clinical utility.

Author Correction: Clinical Implications of Sub-grouping HER2 Positive Tumors by Amplicon Structure and Co-amplified Genes.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Clinical Implications of Sub-grouping HER2 Positive Tumors by Amplicon Structure and Co-amplified Genes.

ERBB2 amplification is a prognostic marker for aggressive tumors and a predictive marker for prolonged survival following treatment with HER2 inhibitors. We attempt to sub-group HER2+ tumors based on amplicon structures and co-amplified genes. We examined five HER2+ cell lines, three HER2+ xenographs and 57 HER2+ tumor tissues. ERBB2 amplification was analyzed using digital droplet PCR and low coverage whole genome sequencing. In some HER2+ tumors PPM1D, that encodes WIP1, is co-amplified. Cell lines were treated with HER2 and WIP1 inhibitors. We find that inverted duplication is the amplicon structure in the majority of HER2+ tumors. In patients suffering from an early stage disease the ERBB2 amplicon is composed of a single segment while in patients suffering from advanced cancer the amplicon is composed of several different segments. We find robust WIP1 inhibition in some HER2+ PPM1D amplified cell lines. Sub-grouping HER2+ tumors using low coverage whole genome sequencing identifies inverted duplications as the main amplicon structure and based on the number of segments, differentiates between local and advanced tumors. In addition, we found that we could determine if a tumor is a recurrent tumor or second primary tumor and identify co-amplified oncogenes that may serve as targets for therapy.