Stereochemistry of Polypeptide Conformation in Coarse Grained Analysis

The conformations available to polypeptides are determined by the interatomic forces acting on the peptide units, whereby backbone torsion angles are restricted as described by the Ramachandran plot. Although typical proteins are composed predominantly from {\alpha}-helices and {\beta}-sheets, they nevertheless adopt diverse tertiary structure, each folded as dictated by its unique amino-acid sequence. Despite such uniqueness, however, the functioning of many proteins involves changes between quite different conformations. The study of large-scale conformational changes, particularly in large systems, is facilitated by a coarse-grained representation such as provided by virtually bonded C{\alpha} atoms. We have developed a virtual atom molecular mechanics (VAMM) force field to describe conformational dynamics in proteins and a VAMM-based algorithm for computing conformational transition pathways. Here we describe the stereochemical analysis of proteins in this coarse-grained representation, comparing the relevant plots in coarse-grained conformational space to the corresponding Ramachandran plots, having contoured each at levels determined statistically from residues in a large database. The distributions shown for an all-{\alpha} protein, two all-{\beta} proteins and one {\alpha}+{\beta} protein serve to relate the coarse-grained distributions to the familiar Ramachandran plot.Comment: 12 pages, 3 figures, Postprint of book chapter submitted to the Biomolecular Forms and Functions, M. Bansal and N. Srinivasan, Eds. copyright (2013) [copyright World Scientific Publishing Company

Mapping the Functional Interactions at the Tumor-Immune Checkpoint Interface

The interactions between tumor intrinsic processes and immune checkpoints can mediate immune evasion by cancer cells and responses to immunotherapy. It is, however, challenging to identify functional interactions due to the prohibitively complex molecular landscape of the tumor-immune interfaces. We address this challenge with a statistical analysis framework, immuno-oncology gene interaction maps (ImogiMap). ImogiMap quantifies and statistically validates tumor-immune checkpoint interactions based on their co-associations with immune-associated phenotypes. The outcome is a catalog of tumor-immune checkpoint interaction maps for diverse immune-associated phenotypes. Applications of ImogiMap recapitulate the interaction of SERPINB9 and immune checkpoints with interferon gamma (IFNγ) expression. Our analyses suggest that CD86-CD70 and CD274-CD70 immunoregulatory interactions are significantly associated with IFNγ expression in uterine corpus endometrial carcinoma and basal-like breast cancer, respectively. The open-source ImogiMap software and user-friendly web application will enable future applications of ImogiMap. Such applications may guide the discovery of previously unknown tumor-immune interactions and immunotherapy targets

Spatial normalization of reverse phase protein array data.

Reverse phase protein arrays (RPPA) are an efficient, high-throughput, cost-effective method for the quantification of specific proteins in complex biological samples. The quality of RPPA data may be affected by various sources of error. One of these, spatial variation, is caused by uneven exposure of different parts of an RPPA slide to the reagents used in protein detection. We present a method for the determination and correction of systematic spatial variation in RPPA slides using positive control spots printed on each slide. The method uses a simple bi-linear interpolation technique to obtain a surface representing the spatial variation occurring across the dimensions of a slide. This surface is used to calculate correction factors that can normalize the relative protein concentrations of the samples on each slide. The adoption of the method results in increased agreement between technical and biological replicates of various tumor and cell-line derived samples. Further, in data from a study of the melanoma cell-line SKMEL-133, several slides that had previously been rejected because they had a coefficient of variation (CV) greater than 15%, are rescued by reduction of CV below this threshold in each case. The method is implemented in the R statistical programing language. It is compatible with MicroVigene and SuperCurve, packages commonly used in RPPA data analysis. The method is made available, along with suggestions for implementation, at http://bitbucket.org/rppa_preprocess/rppa_preprocess/src

Mutant p53 Protects Triple-Negative Breast Adenocarcinomas From Ferroptosis In Vivo

The TP53 tumor suppressor gene is mutated early in most of the patients with triple-negative breast cancer (TNBC). The most frequent TP53 alterations are missense mutations that contribute to tumor aggressiveness. Here, we used an autochthonous somatic TNBC mouse model, in which mutant p53 can be toggled on and off genetically while leaving the tumor microenvironment intact and wild-type for p53 to identify physiological dependencies on mutant p53. In TNBCs that develop in this model, deletion of two different hotspot p53R172H and p53R245W mutants triggers ferroptosis in vivo, a cell death mechanism involving iron-dependent lipid peroxidation. Mutant p53 protects cells from ferroptosis inducers, and ferroptosis inhibitors reverse the effects of mutant p53 loss in vivo. Single-cell transcriptomic data revealed that mutant p53 protects cells from undergoing ferroptosis through NRF2-dependent regulation of Mgst3 and Prdx6, which encode two glutathione-dependent peroxidases that detoxify lipid peroxides. Thus, mutant p53 protects TNBCs from ferroptotic death

KRAS Allelic Variants in Biliary Tract Cancers

IMPORTANCE: Biliary tract cancers (BTCs) contain several actionable molecular alterations, including FGFR2, IDH1, ERBB2 (formerly HER2), and KRAS. KRAS allelic variants are found in 20% to 30% of BTCs, and multiple KRAS inhibitors are currently under clinical investigation. OBJECTIVES: To describe the genomic landscape, co-sequence variations, immunophenotype, genomic ancestry, and survival outcomes of KRAS-mutated BTCs and to calculate the median overall survival (mOS) for the most common allelic variants. DESIGN, SETTING, AND PARTICIPANTS: This retrospective, multicenter, pooled cohort study obtained clinical and next-generation sequencing data from multiple databases between January 1, 2017, and December 31, 2022. These databases included Princess Margaret Cancer Centre, MD Anderson Cancer Center, Foundation Medicine, American Association for Cancer Research Project GENIE, and cBioPortal for Cancer Genomics. The cohort comprised patients with BTCs who underwent genomic testing. MAIN OUTCOME AND MEASURE: The main outcome was mOS, defined as date of diagnosis to date of death, which was measured in months. RESULTS: A total of 7457 patients (n = 3773 males [50.6%]; mean [SD] age, 63 [5] years) with BTCs and genomic testing were included. Of these patients, 5813 had clinical outcome data available, in whom 1000 KRAS-mutated BTCs were identified. KRAS allelic variants were highly prevalent in perihilar cholangiocarcinoma (28.6%) and extrahepatic cholangiocarcinoma (36.1%). Thirty-six KRAS allelic variants were identified, and the prevalence rates in descending order were G12D (41%), G12V (23%), and Q61H (8%). The variant G12D had the highest mOS of 25.1 (95% CI, 22.0-33.0) months compared with 22.8 (95% CI, 19.6-31.4) months for Q61H and 17.8 (95% CI, 16.3-23.1) months for G12V variants. The majority of KRAS-mutated BTCs (98.9%) were not microsatellite instability-high and had low tumor mutational burden (ranging from a median [IQR] of 1.2 (1.2-2.5) to a mean [SD] of 3.3 [1.3]). Immune profiling through RNA sequencing of KRAS and NRAS-mutated samples showed a pattern toward a more immune-inflamed microenvironment with higher M1 macrophage activation (0.16 vs 0.12; P = .047) and interferon-γ expression compared with wild-type tumors. The G12D variant remained the most common KRAS allelic variant in all patient ancestries. Patients with admixed American ancestry had the highest proportion of G12D variant (45.0%). CONCLUSIONS AND RELEVANCE: This cohort study found that KRAS allelic variants were relatively common and may be potentially actionable genomic alterations in patients with BTCs, especially perihilar cholangiocarcinoma and extrahepatic cholangiocarcinoma. The findings add to the growing data on genomic and immune landscapes of KRAS allelic variants in BTCs and are potentially of value to the planning of specific therapies for this heterogeneous patient group

Perturbation biology: inferring signaling networks in cellular systems.

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology

Co-clinical Trial of Novel Bispecific Anti-HER2 Antibody Zanidatamab in Patient-Derived Xenografts

Zanidatamab is a bispecific human epidermal growth factor receptor 2 (HER2)-targeted antibody that has demonstrated antitumor activity in a broad range of HER2-amplified/expressing solid tumors. We determined the antitumor activity of zanidatamab in patient-derived xenograft (PDX) models developed from pretreatment or postprogression biopsies on the first-in-human zanidatamab phase I study (NCT02892123). Of 36 tumors implanted, 19 PDX models were established (52.7% take rate) from 17 patients. Established PDXs represented a broad range of HER2-expressing cancers, and in vivo testing demonstrated an association between antitumor activity in PDXs and matched patients in 7 of 8 co-clinical models tested. We also identified amplification of MET as a potential mechanism of acquired resistance to zanidatamab and demonstrated that MET inhibitors have single-agent activity and can enhance zanidatamab activity in vitro and in vivo. These findings provide evidence that PDXs can be developed from pretreatment biopsies in clinical trials and may provide insight into mechanisms of resistance

Monitoring Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Using Circulating Tumor DNA

BACKGROUND: Triple negative breast cancer (TNBC) is an aggressive subtype with poor prognosis. We aimed to determine whether circulating tumor DNA (ctDNA) and circulating tumor cell (CTC) could predict response and long-term outcomes to neoadjuvant chemotherapy (NAC). METHODS: Patients with TNBC were enrolled between 2017-2021 at The University of Texas MD Anderson Cancer Center (Houston, TX). Serial plasma samples were collected at four timepoints: pre-NAC (baseline), 12-weeks after NAC (mid-NAC), after NAC/prior to surgery (post-NAC), and one-year after surgery. ctDNA was quantified using a tumor-informed ctDNA assay (Signatera RESULTS: In total, 37 patients were enrolled. The mean age was 50 and majority of patients had invasive ductal carcinoma (34, 91.9%) with clinical T2, (25, 67.6%) node-negative disease (21, 56.8%). Baseline ctDNA was detected in 90% (27/30) of patients, of whom 70.4% (19/27) achieved ctDNA clearance by mid-NAC. ctDNA clearance at mid-NAC was significantly associated with pathologic complete response (p = 0.02), whereas CTC clearance was not (p = 0.52). There were no differences in overall survival (OS) and recurrence-free survival (RFS) with positive baseline ctDNA and CTC. However, positive ctDNA at mid-NAC was significantly associated with worse OS and RFS (p = 0.0002 and p = 0.0034, respectively). CONCLUSIONS: Early clearance of ctDNA served as a predictive and prognostic marker in TNBC. Personalized ctDNA monitoring during NAC may help predict response and guide treatment

Longitudinal Dynamic Contrast-Enhanced MRI Radiomic Models for Early Prediction of Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer

Early prediction of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) patients could help oncologists select individualized treatment and avoid toxic effects associated with ineffective therapy in patients unlikely to achieve pathologic complete response (pCR). The objective of this study is to evaluate the performance of radiomic features of the peritumoral and tumoral regions from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired at different time points of NAST for early treatment response prediction in TNBC. This study included 163 Stage I-III patients with TNBC undergoing NAST as part of a prospective clinical trial (NCT02276443). Peritumoral and tumoral regions of interest were segmented on DCE images at baseline (BL) and after two (C2) and four (C4) cycles of NAST. Ten first-order (FO) radiomic features and 300 gray-level-co-occurrence matrix (GLCM) features were calculated. Area under the receiver operating characteristic curve (AUC) and Wilcoxon rank sum test were used to determine the most predictive features. Multivariate logistic regression models were used for performance assessment. Pearson correlation was used to assess intrareader and interreader variability. Seventy-eight patients (48%) had pCR (52 training, 26 testing), and 85 (52%) had non-pCR (57 training, 28 testing). Forty-six radiomic features had AUC at least 0.70, and 13 multivariate models had AUC at least 0.75 for training and testing sets. The Pearson correlation showed significant correlation between readers. In conclusion, Radiomic features from DCE-MRI are useful for differentiating pCR and non-pCR. Similarly, predictive radiomic models based on these features can improve early noninvasive treatment response prediction in TNBC patients undergoing NAST

Multiparametric MRI-Based Radiomic Models for Early Prediction of Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is often treated with neoadjuvant systemic therapy (NAST). We investigated if radiomic models based on multiparametric Magnetic Resonance Imaging (MRI) obtained early during NAST predict pathologic complete response (pCR). We included 163 patients with stage I-III TNBC with multiparametric MRI at baseline and after 2 (C2) and 4 cycles of NAST. Seventy-eight patients (48%) had pCR, and 85 (52%) had non-pCR. Thirty-six multivariate models combining radiomic features from dynamic contrast-enhanced MRI and diffusion-weighted imaging had an area under the receiver operating characteristics curve (AUC) \u3e 0.7. The top-performing model combined 35 radiomic features of relative difference between C2 and baseline; had an AUC = 0.905 in the training and AUC = 0.802 in the testing set. There was high inter-reader agreement and very similar AUC values of the pCR prediction models for the 2 readers. Our data supports multiparametric MRI-based radiomic models for early prediction of NAST response in TNBC