Broad Anti-Cancer Activity Produced by Targeted Nutrients Deprivation (TND) of Multiple Non-Essential Amino Acids.

It has been known for close to 100 years that the metabolism of cancer cells is altered and different than that of healthy cells in the body. On that basis, we have developed an entirely novel approach to managing cancer, termed Targeted Nutrients Deprivation (TND). TND employs a formulated diet depleted of multiple non-essential amino acids (NEAAs) that are required by tumor cells but not by normal cells. Cancer cells specifically require those NEAAs due to their heightened and rewired metabolism. We demonstrated that our first proprietary formulated TND diet-FTN203-significantly reduced the growth of multiple human tumor xenografts in mouse. In combination with chemotherapy and immunotherapy, FTN203 further enhanced therapeutic efficacy. Reliance on FTN203 as the sole nutrition source was shown to be safe without causing detrimental body-weight loss or internal organ damage. Our findings indicate that TND is a novel and safe approach to managing cancer.Supplemental data for this article is available online at https://doi.org/10.1080/01635581.2021.2013904 .

Oncogenic activating mutations are associated with local copy gain.

Although activating mutations and gains in copy number are key mechanisms for oncogene activation, the relationship between the two is not well understood. In this study, we focused on KRAS copy gains and mutations in non-small cell lung cancer. We found that KRAS copy gains occur more frequently in tumors with KRAS activating mutations and are associated with large increases in KRAS expression. These copy gains tend to be more focal in tumors with activating mutations than in those with wild-type KRAS. Fluorescence in situ hybridization analysis revealed that some tumors have homogeneous low-level gains of the KRAS locus, whereas others have high-level amplification of KRAS, often in only a fraction of tumor cells. Associations between activating mutation and copy gains were also observed for other oncogenes (EGFR in non-small cell lung cancer, BRAF and NRAS in melanoma). Activating mutations were associated with copy gains only at the mutated oncogene locus but not other oncogene loci. However, KRAS activating mutations in colorectal cancer were not associated with copy gains. Future work is warranted to clarify the relationship among the different mechanisms of oncogene activation.

High-resolution analysis of copy number alterations and associated expression changes in ovarian tumors.

BACKGROUND: DNA copy number alterations are frequently observed in ovarian cancer, but it remains a challenge to identify the most relevant alterations and the specific causal genes in those regions. METHODS: We obtained high-resolution 500K SNP array data for 52 ovarian tumors and identified the most statistically significant minimal genomic regions with the most prevalent and highest-level copy number alterations (recurrent CNAs). Within a region of recurrent CNA, comparison of expression levels in tumors with a given CNA to tumors lacking that CNA and to whole normal ovary samples was used to select genes with CNA-specific expression patterns. A public expression array data set of laser capture micro-dissected (LCM) non-malignant fallopian tube epithelia and LCM ovarian serous adenocarcinoma was used to evaluate the effect of cell-type mixture biases. RESULTS: Fourteen recurrent deletions were detected on chromosomes 4, 6, 9, 12, 13, 15, 16, 17, 18, 22 and most prevalently on X and 8. Copy number and expression data suggest several apoptosis mediators as candidate drivers of the 8p deletions. Sixteen recurrent gains were identified on chromosomes 1, 2, 3, 5, 8, 10, 12, 15, 17, 19, and 20, with the most prevalent gains localized to 8q and 3q. Within the 8q amplicon, PVT1, but not MYC, was strongly over-expressed relative to tumors lacking this CNA and showed over-expression relative to normal ovary. Likewise, the cell polarity regulators PRKCI and ECT2 were identified as putative drivers of two distinct amplicons on 3q. Co-occurrence analyses suggested potential synergistic or antagonistic relationships between recurrent CNAs. Genes within regions of recurrent CNA showed an enrichment of Cancer Census genes, particularly when filtered for CNA-specific expression. CONCLUSION: These analyses provide detailed views of ovarian cancer genomic changes and highlight the benefits of using multiple reference sample types for the evaluation of CNA-specific expression changes.

Genetic alterations and oncogenic pathways associated with breast cancer subtypes.

Breast cancers can be divided into subtypes with important implications for prognosis and treatment. We set out to characterize the genetic alterations observed in different breast cancer subtypes and to identify specific candidate genes and pathways associated with subtype biology. mRNA expression levels of estrogen receptor, progesterone receptor, and HER2 were shown to predict marker status determined by immunohistochemistry and to be effective at assigning samples to subtypes. HER2(+) cancers were shown to have the greatest frequency of high-level amplification (independent of the ERBB2 amplicon itself), but triple-negative cancers had the highest overall frequencies of copy gain. Triple-negative cancers also were shown to have more frequent loss of phosphatase and tensin homologue and mutation of RB1, which may contribute to genomic instability. We identified and validated seven regions of copy number alteration associated with different subtypes, and used integrative bioinformatics analysis to identify candidate oncogenes and tumor suppressors, including ERBB2, GRB7, MYST2, PPM1D, CCND1, HDAC2, FOXA1, and RASA1. We tested the candidate oncogene MYST2 and showed that it enhances the anchorage-independent growth of breast cancer cells. The genome-wide and region-specific differences between subtypes suggest the differential activation of oncogenic pathways.

High-resolution genomic and expression analyses of copy number alterations in breast tumors.

Analysis of recurrent DNA amplification can lead to the identification of cancer driver genes, but this process is often hampered by the low resolution of existing copy number analysis platforms. Fifty-one breast tumors were profiled for copy number alterations (CNAs) with the high-resolution Affymetrix 500K SNP array. These tumors were also expression-profiled and surveyed for mutations in selected genes commonly mutated in breast cancer (TP53, CDKN2A, ERBB2, KRAS, PIK3CA, PTEN). Combined analysis of common CNAs and mutations revealed putative associations between features. Analysis of both the prevalence and amplitude of CNAs defined regions of recurrent alteration. Compared with previous array comparative genomic hybridization studies, our analysis provided boundaries for frequently altered regions that were approximately one-fourth the size, greatly reducing the number of potential alteration-driving genes. Expression data from matched tumor samples were used to further interrogate the functional relevance of genes located in recurrent amplicons. Although our data support the importance of some known driver genes such as ERBB2, refined amplicon boundaries at other locations, such as 8p11-12 and 11q13.5-q14.2, greatly reduce the number of potential driver genes and indicate alternatives to commonly suggested driver genes in some cases. For example, the previously reported recurrent amplification at 17q23.2 is reduced to a 249 kb minimal region containing the putative driver RPS6KB1 as well as the putative oncogenic microRNA mir-21. High-resolution copy number analysis provides refined insight into many breast cancer amplicons and their relationships to gene expression, point mutations and breast cancer subtype classifications. This article contains Supplementary Material available at http://www.interscience.wiley.com/jpages/1045-2257/suppmat.

PADGE: analysis of heterogeneous patterns of differential gene expression.

UNLABELLED: We have devised a novel analysis approach, percentile analysis for differential gene expression (PADGE), for identifying genes differentially expressed between two groups of heterogeneous samples. PADGE was designed to compare expression profiles of sample subgroups at a series of percentile cutoffs and to examine the trend of relative expression between sample groups as expression level increases. Simulation studies showed that PADGE has more statistical power than t-statistics, cancer outlier profile analysis (COPA) (Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S, Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, Chinnaiyan AM. Science 310: 644-648, 2005), and kurtosis (Teschendorff AE, Naderi A, Barbosa-Morais NL, Caldas C. Bioinformatics 22: 2269-2275, 2006). Application of PADGE to microarray data sets in tumor tissues demonstrated its utility in prioritizing cancer genes encoding potential therapeutic targets or diagnostic markers. A web application was developed for researchers to analyze a large gene expression data set from heterogeneous biological samples and identify differentially expressed genes between subsets of sample classes using PADGE and other available approaches. AVAILABILITY: http://www.cgl.ucsf.edu/Research/genentech/padge/.

Protein-interaction mapping in search of effective drug targets.

Signaling complexes and networks are being intensely studied in an attempt to discover pathways that are amenable to therapeutic intervention. A challenge in this search is to understand the effect that the modulation of a target will have on the overall function of a cell and its surrounding neighbors. Protein-interaction mapping reveals relationships between proteins and their impact on cellular processes and is being used more widely in our understanding of disease mechanisms and their treatment. The review discusses challenges and breakthroughs in this new and evolving area and its impact on medicine.

Rax1, a protein required for the establishment of the bipolar budding pattern in yeast.

In Saccharomyces cerevisiae, cell type determines two distinct spatial budding patterns. Haploid cells exhibit an axial pattern, whereas diploid cells exhibit a bipolar pattern. Axl1, a member of the insulin-degrading enzyme (IDE) family, is the key morphological determinant for the haploid axial pattern. Here we identified a novel gene, RAX1, specifically required for the bipolar budding pattern. Loss of RAX1 alters the bipolar pattern of axl1 haploids resulting in reversion to the axial pattern, and also alters the bipolar patterns of bud3 and bud4 haploids. However, bud10 rax1 haploids exhibit a random budding pattern, suggesting Bud10 acts as the key proximal landmark in axial budding. Rax1 is required for the localization of Bud8, the distal bipolar budding landmark. Interestingly, Rax1 contains a C-terminal domain possessing some similarity to insulin-related peptides. Our results suggest that Rax1 is necessary for the establishment of the bipolar budding landmark.

Gaining confidence in high-throughput protein interaction networks.

Although genome-scale technologies have benefited from statistical measures of data quality, extracting biologically relevant pathways from high-throughput proteomics data remains a challenge. Here we develop a quantitative method for evaluating proteomics data. We present a logistic regression approach that uses statistical and topological descriptors to predict the biological relevance of protein-protein interactions obtained from high-throughput screens for yeast. Other sources of information, including mRNA expression, genetic interactions and database annotations, are subsequently used to validate the model predictions without bias or cross-pollution. Novel topological statistics show hierarchical organization of the network of high-confidence interactions: protein complex interactions extend one to two links, and genetic interactions represent an even finer scale of organization. Knowledge of the maximum number of links that indicates a significant correlation between protein pairs (correlation distance) enables the integrated analysis of proteomics data with data from genetics and gene expression. The type of analysis presented will be essential for analyzing the growing amount of genomic and proteomics data in model organisms and humans.

Subcellular localization of Axl1, the cell type-specific regulator of polarity.

Bud-site selection in yeast offers an attractive system for studying cell polarity and asymmetric division. Haploids divide in an axial pattern, whereas diploids divide in a bipolar pattern. AXL1 is expressed in haploids but not diploids, and ectopic expression of AXL1 in diploids converts their bipolar budding pattern to an axial pattern. How Axl1 acts as a switch between the bipolar and axial patterns is not understood. Here we report that Axl1 localizes to the mother-bud neck and division site remnants of haploids. Axl1 is absent from diploids. Axl1 colocalizes with Bud3, Bud4, and Bud10, components of the axial landmark structure. This localization suggests that Axl1 couples the axial landmark with downstream polarity establishment factors. Consistent with such a role, Axl1 associated biochemically with Bud4 and Bud5. Genetic evidence suggests that Axl1 works with Bud3 and Bud4 to promote the activity of the Bud10 membrane protein. Given Axl1's suggested role in morphogenesis and cell fusion during mating, we also examined its localization during this process. Axl1 redistributes independently of the axial landmark to a tight cell surface dot at the tip of each mating projection. These dots are rapidly lost as prezygotes form.