Longitudinal profiling identifies co-occurring BRCA1/2 reversions, TP53BP1, RIF1 and PAXIP1 mutations in PARP inhibitor-resistant advanced breast cancer.

BACKGROUND: Resistance to therapies that target homologous recombination deficiency (HRD) in breast cancer limits their overall effectiveness. Multiple, preclinically validated, mechanisms of resistance have been proposed, but their existence and relative frequency in clinical disease are unclear, as is how to target resistance. PATIENTS AND METHODS: Longitudinal mutation and methylation profiling of circulating tumour (ct)DNA was carried out in 47 patients with metastatic BRCA1-, BRCA2- or PALB2-mutant breast cancer treated with HRD-targeted therapy who developed progressive disease-18 patients had primary resistance and 29 exhibited response followed by resistance. ctDNA isolated at multiple time points in the patient treatment course (before, on-treatment and at progression) was sequenced using a novel >750-gene intron/exon targeted sequencing panel. Where available, matched tumour biopsies were whole exome and RNA sequenced and also used to assess nuclear RAD51. RESULTS: BRCA1/2 reversion mutations were present in 60% of patients and were the most prevalent form of resistance. In 10 cases, reversions were detected in ctDNA before clinical progression. Two new reversion-based mechanisms were identified: (i) intragenic BRCA1/2 deletions with intronic breakpoints; and (ii) intragenic BRCA1/2 secondary mutations that formed novel splice acceptor sites, the latter being confirmed by in vitro minigene reporter assays. When seen before commencing subsequent treatment, reversions were associated with significantly shorter time to progression. Tumours with reversions retained HRD mutational signatures but had functional homologous recombination based on RAD51 status. Although less frequent than reversions, nonreversion mechanisms [loss-of-function (LoF) mutations in TP53BP1, RIF1 or PAXIP1] were evident in patients with acquired resistance and occasionally coexisted with reversions, challenging the notion that singular resistance mechanisms emerge in each patient. CONCLUSIONS: These observations map the prevalence of candidate drivers of resistance across time in a clinical setting, information with implications for clinical management and trial design in HRD breast cancers.

Grouping 34 Chemicals Based on Mode of Action Using Connectivity Mapping.

Connectivity mapping is a method used in the pharmaceutical industry to find connections between small molecules, disease states, and genes. The concept can be applied to a predictive toxicology paradigm to find connections between chemicals, adverse events, and genes. In order to assess the applicability of the technique for predictive toxicology purposes, we performed gene array experiments on 34 different chemicals: bisphenol A, genistein, ethinyl-estradiol, tamoxifen, clofibrate, dehydorepiandrosterone, troglitazone, diethylhexyl phthalate, flutamide, trenbolone, phenobarbital, retinoic acid, thyroxine, 1α,25-dihydroxyvitamin D3, clobetasol, farnesol, chenodeoxycholic acid, progesterone, RU486, ketoconazole, valproic acid, desferrioxamine, amoxicillin, 6-aminonicotinamide, metformin, phenformin, methotrexate, vinblastine, ANIT (1-naphthyl isothiocyanate), griseofulvin, nicotine, imidacloprid, vorinostat, 2,3,7,8-tetrachloro-dibenzo-p-dioxin (TCDD) at the 6-, 24-, and 48-hour time points for 3 different concentrations in the 4 cell lines: MCF7, Ishikawa, HepaRG, and HepG2 GEO (super series accession no.: GSE69851). The 34 chemicals were grouped in to predefined mode of action (MOA)-based chemical classes based on current literature. Connectivity mapping was used to find linkages between each chemical and between chemical classes. Cell line-specific linkages were compared with each other and to test whether the method was platform and user independent, a similar analysis was performed against publicly available data. The study showed that the method can group chemicals based on MOAs and the inter-chemical class comparison alluded to connections between MOAs that were not predefined. Comparison to the publicly available data showed that the method is user and platform independent. The results provide an example of an alternate data analysis process for high-content data, beneficial for predictive toxicology, especially when grouping chemicals for read across purposes.