Low-Frequency Mutational Heterogeneity of Invasive Ductal Carcinoma Subtypes: Information to Direct Precision Oncology

CarcSeq Measurement of Rat Mammary Cancer Driver Mutations and Relation to Spontaneous Mammary Neoplasia

The ability to deduce carcinogenic potential from subchronic, repeat dose rodent studies would constitute a major advance in chemical safety assessment and drug development. This study investigated an error-corrected NGS method (CarcSeq) for quantifying cancer driver mutations (CDMs) and deriving a metric of clonal expansion predictive of future neoplastic potential. CarcSeq was designed to interrogate subsets of amplicons encompassing hotspot CDMs applicable to a variety of cancers. Previously, normal human breast DNA was analyzed by CarcSeq and metrics based on mammary-specific CDMs were correlated with tissue donor age, a surrogate of breast cancer risk. Here we report development of parallel methodologies for rat. The utility of the rat CarcSeq method for predicting neoplastic potential was investigated by analyzing mammary tissue of 16-week-old untreated rats with known differences in spontaneous mammary neoplasia (Fischer 344, Wistar Han, and Sprague Dawley). Hundreds of mutants with mutant fractions ≥ 10-4 were quantified in each strain, most were recurrent mutations, and 42.5% of the nonsynonymous mutations have human homologs. Mutants in the mammary-specific target of the most tumor-sensitive strain (Sprague Dawley) showed the greatest nonsynonymous/synonymous mutation ratio, indicative of positive selection consistent with clonal expansion. For the mammary-specific target (Hras, Pik3ca, and Tp53 amplicons), median absolute deviation correlated with percentages of rats that develop spontaneous mammary neoplasia at 104 weeks (Pearson r = 1.0000, 1-tailed p = .0010). Therefore, this study produced evidence CarcSeq analysis of spontaneously occurring CDMs can be used to derive an early metric of clonal expansion relatable to long-term neoplastic outcome.

Assessment of clonal expansion using CarcSeq measurement of lung cancer driver mutations and correlation with mouse strain- and sex-related incidence of spontaneous lung neoplasia

Quantification of variation in levels of spontaneously occurring cancer driver mutations (CDMs) was developed to assess clonal expansion and predict future risk of neoplasm development. Specifically, an error-corrected next generation sequencing method, CarcSeq, and a mouse CarcSeq panel (analogous to human and rat panels) were developed and used to quantify low-frequency mutations in a panel of amplicons enriched in hotspot CDMs. Mutations in a subset of panel amplicons, Braf, Egfr, Kras, Stk11 and Tp53, were related to incidence of lung neoplasms at two years. This was achieved by correlating median absolute deviation (MAD) from the overall median mutant fraction (MF) measured in the lung DNA of 16-week-old male and female, B6C3F1 and CD-1 mice (10 mice/sex/strain) with percentages of spontaneous alveolar/bronchioloalveolar adenomas and carcinomas reported in bioassay control groups. 1,586 mouse lung mutants with MFs >1 x 10-4 were recovered. The ratio of non-synonymous to synonymous mutations was used to assess the proportion of recovered mutations conferring a positive selective advantage. The greatest ratio was observed in what is considered the most lung tumor-sensitive model examined, male B6C3F1 mice. Of the recurrent, non-synonymous mouse mutations recovered, 55.5% have been reported in human tumors, with many located in or around the mouse equivalent of human cancer hotspot codons. MAD for the same subset of amplicons measured in normal human lung DNA samples showed a correlation of moderate strength and borderline significance) with age (a cancer risk factor), as well as age-related cumulative lung cancer risk, suggesting MAD may inform species extrapolation.

The Correlation of Mutations and Expressions of Genes within the PI3K/Akt/mTOR Pathway in Breast Cancer-A Preliminary Study

There is an urgent need to seek new molecular biomarkers helpful in diagnosing and treating breast cancer. In this elaboration, we performed a molecular analysis of mutations and expression of genes within the PI3K/Akt/mTOR pathway in patients with ductal breast cancer of various malignancy levels. We recognized significant correlations between the expression levels of the studied genes. We also performed a bioinformatics analysis of the data available on the international database TCGA and compared them with our own research. Studies on mutations and expression of genes were conducted using High-Resolution Melt PCR (HRM-PCR), Allele-Specific-quantitative PCR (ASP-qPCR), Real-Time PCR molecular methods in a group of women with ductal breast cancer. Bioinformatics analysis was carried out using web source Ualcan and bc-GenExMiner. In the studied group of women, it was observed that the prevalence of mutations in the studied PIK3CA and AKT1 genes was 29.63%. It was stated that the average expression level of the PIK3CA, PIK3R1, PTEN genes in the group of breast cancer patients is lower in comparison to the control group, while the average expression level of the AKT1 and mTOR genes in the studied group was higher in comparison to the control group. It was also indicated that in the group of patients with mutations in the area of the PIK3CA and AKT1 genes, the PIK3CA gene expression level is statistically significantly lower than in the group without mutations. According to our knowledge, we demonstrate, for the first time, that there is a very strong positive correlation between the levels of AKT1 and mTOR gene expression in the case of patients with mutations and without mutations.

Quantification of cancer driver mutations in human breast and lung DNA using targeted, error-corrected CarcSeq

There is a need for scientifically-sound, practical approaches to improve carcinogenicity testing. Advances in DNA sequencing technology and knowledge of events underlying cancer development have created an opportunity for progress in this area. The long-term goal of this work is to develop variation in cancer driver mutation (CDM) levels as a metric of clonal expansion of cells carrying CDMs because these important early events could inform carcinogenicity testing. The first step toward this goal was to develop and validate an error-corrected next-generation sequencing method to analyze panels of hotspot cancer driver mutations (hCDMs). The "CarcSeq" method that was developed uses unique molecular identifier sequences to construct single-strand consensus sequences for error correction. CarcSeq was used for mutational analysis of 13 amplicons encompassing >20 hotspot CDMs in normal breast, normal lung, ductal carcinomas, and lung adenocarcinomas. The approach was validated by detecting expected differences related to tissue type (normal vs. tumor and breast vs. lung) and mutation spectra. CarcSeq mutant fractions (MFs) correlated strongly with previously obtained ACB-PCR mutant fraction (MF) measurements from the same samples. A reconstruction experiment, in conjunction with other analyses, showed CarcSeq accurately quantifies MFs ≥10-4 . CarcSeq MF measurements were correlated with tissue donor age and breast cancer risk. CarcSeq MF measurements were correlated with deviation from median MFs analyzed to assess clonal expansion. Thus, CarcSeq is a promising approach to advance cancer risk assessment and carcinogenicity testing practices. Paradigms that should be investigated to advance this strategy for carcinogenicity testing are proposed.

ACB-PCR Quantification of Low-Frequency Hotspot Cancer-Driver Mutations

Allele-specific competitive blocker PCR (ACB-PCR) is a sensitive and quantitative approach for the selective amplification of a specific base substitution. Using the ACB-PCR technique, hotspot cancer-driver mutations (tumor-relevant mutations in oncogenes and tumor suppressor genes, which confer a selective growth advantage) are being developed as quantitative biomarkers of cancer risk. ACB-PCR employs a mutant-specific primer (with a 3'-penultimate mismatch relative to the mutant DNA sequence, but a double 3'-terminal mismatch relative to the wild-type DNA sequence) to selectively amplify rare mutant DNA molecules. A blocker primer having a non-extendable 3'-end and a 3'-penultimate mismatch relative to the wild-type DNA sequence, but a double 3'-terminal mismatch relative to the mutant DNA sequence is included in ACB-PCR to selectively repress amplification from abundant wild-type molecules. Consequently, ACB-PCR can quantify the level of a single base pair substitution mutation in a DNA population when present at a mutant:wild-type ratio of 1 × 10^-5 or greater. Quantification of rare mutant alleles is achieved by parallel analysis of unknown samples and mutant fraction (MF) standards (defined mixtures of mutant and wild-type DNA sequences). The ability to quantify specific mutations with known association to cancer has several important applications in evaluating the carcinogenic potential of chemical exposures in rodent models. Further, the measurement of cancer-driver mutant subpopulations is important for precision cancer treatment (selecting the most appropriate targeted therapy and predicting the development of therapeutic resistance). This chapter provides a step-by-step description of the ACB-PCR methodology as it has been used to measure human PIK3CA codon 1047, CAT→CGT (H1047R) mutation.

Outgrowth of erlotinib-resistant subpopulations recapitulated in patient-derived lung tumor spheroids and organoids

A model that recapitulates development of acquired therapeutic resistance is needed to improve oncology drug development and patient outcomes. To achieve this end, we established methods for the preparation and growth of spheroids from primary human lung adenocarcinomas, including methods to culture, passage, monitor growth, and evaluate changes in mutational profile over time. Primary lung tumor spheroids were cultured in Matrigel® with varying concentrations of erlotinib, a small molecule kinase inhibitor of epidermal growth factor receptor (EGFR) that is ineffective against KRAS mutant cells. Subtle changes in spheroid size and number were observed within the first two weeks of culture. Spheroids were cultured for up to 24 weeks, during which time interactions between different cell types, movement, and assembly into heterogeneous organoid structures were documented. Allele-specific competitive blocker PCR (ACB-PCR) was used to quantify low frequency BRAF V600E, KRAS G12D, KRAS G12V, and PIK3CA H1047R mutant subpopulations in tumor tissue residue (TR) samples and cultured spheroids. Mutant subpopulations, including multiple mutant subpopulations, were quite prevalent. Twelve examples of mutant enrichment were found in eight of the 14 tumors analyzed, based on the criteria that a statistically-significant increase in mutant fraction was observed relative to both the TR and the no-erlotinib control. Of the mutants quantified in erlotinib-treated cultures, PIK3CA H1047 mutant subpopulations increased most often (5/14 tumors), which is consistent with clinical observations. Thus, this ex vivo lung tumor spheroid model replicates the cellular and mutational tumor heterogeneity of human lung adenocarcinomas and can be used to assess the outgrowth of mutant subpopulations. Spheroid cultures with characterized mutant subpopulations could be used to investigate the efficacy of lung cancer combination therapies.

Rationale and Roadmap for Developing Panels of Hotspot Cancer Driver Gene Mutations as Biomarkers of Cancer Risk

Cancer driver mutations (CDMs) are necessary and causal for carcinogenesis and have advantages as reporters of carcinogenic risk. However, little progress has been made toward developing measurements of CDMs as biomarkers for use in cancer risk assessment. Impediments for using a CDM-based metric to inform cancer risk include the complexity and stochastic nature of carcinogenesis, technical difficulty in quantifying low-frequency CDMs, and lack of established relationships between cancer driver mutant fractions and tumor incidence. Through literature review and database analyses, this review identifies the most promising targets to investigate as biomarkers of cancer risk. Mutational hotspots were discerned within the 20 most mutated genes across the 10 deadliest cancers. Forty genes were identified that encompass 108 mutational hotspot codons overrepresented in the COSMIC database; 424 different mutations within these hotspot codons account for approximately 63,000 tumors and their prevalence across tumor types is described. The review summarizes literature on the prevalence of CDMs in normal tissues and suggests such mutations are direct and indirect substrates for chemical carcinogenesis, which occurs in a spatially stochastic manner. Evidence that hotspot CDMs (hCDMs) frequently occur as tumor subpopulations is presented, indicating COSMIC data may underestimate mutation prevalence. Analyses of online databases show that genes containing hCDMs are enriched in functions related to intercellular communication. In its totality, the review provides a roadmap for the development of tissue-specific, CDM-based biomarkers of carcinogenic potential, comprised of batteries of hCDMs and can be measured by error-correct next-generation sequencing. Environ. Mol. Mutagen. 61:152-175, 2020. Published 2019. This article is a U.S. Government work and is in the public domain in the USA. Environmental and Molecular Mutagenesis published by Wiley Periodicals, Inc. on behalf of Environmental Mutagen Society.