Biomarkers Of Chemotherapy Resistance Research Articles

Transfer RNA-Derived RNAs As Novel Predictive Biomarkers of Chemotherapy Resistance in Breast Cancer

Transfer RNA-Derived RNAs As Novel Predictive Biomarkers of Chemotherapy Resistance in Breast Cancer

Abstract B59: Single-cell sequencing as a prognostic and predictive tool for ovarian cancer therapy

Abstract Technological advances allow genomic and transcriptomic analyses to be conducted at the single-cell level. Analysis of gene expression and DNA sequence at this detailed level could lead to prognostic and predictive biomarkers as well as an enhanced understanding of cancer cell and stromal subpopulations including stem cells and chemotherapy-resistant populations. This knowledge will improve our ability to implement a “precision medicine” approach to the treatment of women with ovarian cancer. We have initiated a project to prospectively study the transcriptome and DNA exome of freshly isolated, high-grade serous ovarian cancer solid tumor samples at the single-cell level. To date we have enrolled 8 patients and have initiated single-cell whole-exome DNA sequencing, using the Fluidigm C1 chip, and single-cell whole-transcriptome RNA sequencing, using the 10X Genomics platform. For comparison of RNA sequencing technologies, we previously performed RNA sequencing of a patient’s sample using the Fluidigm C1 chip. In addition to sequencing the primary tumor samples, we are concurrently attempting to establish patient-derived xenografts (PDXs) and cell lines for each patient. We have successfully generated PDX mice from the first patient and are currently treating a subset of the mice with carboplatin/paclitaxel. Patients will be followed prospectively and information on their clinical response will be incorporated into the analysis. Analysis of the RNA sequencing results from the first patient using the Fluidigm C1 platform identified several distinct populations of cells characterized by gene expression patterns that correlate with specific signaling pathways and disease states. We could clearly differentiate stromal cells from cancer epithelial cells based on gene expression patterns. Furthermore, using known functional markers, we could define subsets within each population, including three subgroups of cancer cells and four subgroups of stromal cells. Based on their gene expression patterns, we could determine the frequency of cancer epithelial cells compared to cancer cells undergoing epithelial-to-mesenchymal transition, as well as activated and nonactivated fibroblasts and myofibroblasts. We were also able to identify rare cells expressing stem cell markers. Using the 10X Genomics platform, we have completed RNA sequencing of five patients (~370 million reads/patient). We quantified gene expression on an average of 8,707 cells/patient and 1,723 genes/cell. Using graph-based clustering combined with the t-Distributed Stochastic Neighbor Embedding technique for high dimensionality reduction, we have identified approximately 9 to 15 subsets of cells within these cancer samples based on an unbiased analysis of their gene expression patterns. Using bioinformatic tools, including CellRanger, Seurat, Ingenuity Pathway Analysis, and Gene Set Enrichment Analysis, we can define several of these subsets based on known functional markers, including subsets of immune cells, stromal cells, and cancer epithelial cells. We can estimate the frequency of each subset and can also subdivide the groups using cell-type specific markers, for example, by distinguishing macrophages from dendritic cells within the immune subset. We will present unpublished data, including RNA sequencing and DNA exome sequencing of single cells on the first 8 enrolled patients. In addition to sequencing the primary tumor samples, we plan on performing single-cell sequencing of matched platinum-resistant tumors generated by treating the PDX mice with carboplatin and paclitaxel. We will also correlate presence and percentage of cell subpopulations with clinical outcomes of the patients. Our long-term goal is to use single-cell data as a prognostic biomarker for chemotherapy resistance as well as a tool for predicting effective therapeutic options, including targeted therapy and immune therapy. Citation Format: Boris J. Winterhoff, Christopher R. Clark, Sidharth Ramesh, Mihir Shetty, Locke Uppendahl, Amit Kumra Mitra, Attila Sebe, Martina Bazzaro, Melissa A. Geller, Juan E. Abrahante, Klein Molly, Raffaele Hellweg, Sally Mullany, Kenneth Beckman, Jerry Daniel, Timothy K. Starr. Single-cell sequencing as a prognostic and predictive tool for ovarian cancer therapy. [abstract]. In: Proceedings of the AACR Conference: Addressing Critical Questions in Ovarian Cancer Research and Treatment; Oct 1-4, 2017; Pittsburgh, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(15_Suppl):Abstract nr B59.

Microdose-Induced Drug-DNA Adducts as Biomarkers of Chemotherapy Resistance in Humans and Mice.

We report progress on predicting tumor response to platinum-based chemotherapy with a novel mass spectrometry approach. Fourteen bladder cancer patients were administered one diagnostic microdose each of [14C]carboplatin (1% of the therapeutic dose). Carboplatin-DNA adducts were quantified by accelerator mass spectrometry in blood and tumor samples collected within 24 hours, and compared with subsequent chemotherapy response. Patients with the highest adduct levels were responders, but not all responders had high adduct levels. Four patient-derived bladder cancer xenograft mouse models were used to test the possibility that another drug in the regimen could cause a response. The mice were dosed with [14C]carboplatin or [14C]gemcitabine and the resulting drug-DNA adduct levels were compared with tumor response to chemotherapy. At least one of the drugs had to induce high drug-DNA adduct levels or create a synergistic increase in overall adducts to prompt a corresponding therapeutic response, demonstrating proof-of-principle for drug-DNA adducts as predictive biomarkers. Mol Cancer Ther; 16(2); 376-87. ©2016 AACR.

Abstract A53: Biomarkers of chemotherapy resistance in serous epithelial ovarian cancer identified by integrative genomic and transcriptomic analysis

Abstract Resistance to platinum-based chemotherapy remains a major impediment in the treatment of serous epithelial ovarian cancer. The objective of this study was to use gene expression and copy number profiling to delineate major deregulated pathways and biomarker networks associated with the development of intrinsic chemotherapy resistance with exposure to standard first-line therapy for ovarian cancer. The study cohort comprised 28 high grade serous ovarian cancer patients divided into two groups based on their varying sensitivity to first-line chemotherapy using progression free survival (PFS) as a surrogate of response. Twelve patient tumors demonstrating relative resistance to platinum based chemotherapy corresponding to shorter PFS (less than 6 months) were compared to 16 tumors from platinum-sensitive patients (PFS more than 18months). Molecular profiling was performed using Affymetrix high-resolution microarray platforms to permit global comparisons of gene expression levels and copy number profiles between tumors from the resistant group with the sensitive group. Microarray data analysis using statistical methods revealed a set of 204 discriminating genes of which expression levels may be influencing differential chemotherapy response between the two groups. Pathway analysis of the differentiating genes showed IGF1 network to be significantly altered between the two groups in addition to PI3K, NFkB, distinguishing the chemotherapy resistant with the sensitive group. Copy number analysis showed differences in the chromosomal regions, 4q31.22, 5q13.2, 9p24.3, 2p23.2, 16q21, 6q14.1, 7p22.3, 12p13 and Xq. Integrative copy number and gene expression profiling will delineate the drivers of chemotherapy resistance in patients undergoing standard platinum-based treatment of ovarian cancer. Future studies to validate these markers are necessary to apply this knowledge to biomarker-based clinical trials. Citation Format: Madhuri Koti, Robert J. Gooding, Paulo Nuin, Alexandria Haslehurst, Colleen Crane, Johanne Weberpals, Timothy Chids, Peter Bryson, Moyez Dharsee, Kenneth R. Evans, Harriet E. Feilotter, Paul C. Park, Jeremy A. Squire. Biomarkers of chemotherapy resistance in serous epithelial ovarian cancer identified by integrative genomic and transcriptomic analysis. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: From Concept to Clinic; Sep 18-21, 2013; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2013;19(19 Suppl):Abstract nr A53.

Is there any difference in overall response rate according to kras mutation in advanced colorectal cancer (aCRC)?

e14589 Background: Tejpar et al (JCO2012; 30(29):3570-7) showed that patients with aCRC and Gly13D kras mutation experienced worse response rate with chemotherapy alone compared to those with other kras mutations. This retrospective study analyzes differences in clinical and radiological response between patients with the two main kras mutations: Gly12Val or Gly13D. Methods: In this retrospective-observational trial, patients with aCRC and Gly12Val and Gly13D kras mutation tumours participated. Clinical and radiological responses of patients with each of the two kras mutation, detected by Therascreen technique and treated with conventional first-line chemotherapy schemes, were recorded. SPSSv17 was employed. ORR (Overall Response Rate: CR+PR) and OCR (Overall Control Rate: CR+PR+SD) were calculated. Results: From November 2005 to October 2012, 51 patients with aCRC and Gly12Val or Gly13D kras mutations were included. Median age was 66 (41-80); 52.7 % men and 49.1% with synchronous disease. Kras mutation status was: 47.3% Gly13D and 52.7% Gly12Val. The chemotherapy schedules were based on Oxaliplatin, Irinotecan and others (61.5%, 25% and 13.5% respectively). While ORR was observed in 39.22 % (38.46 % Gly12Val and 40% Gly13D; p: 0.57), OCR was 76.5% (76.9% Gly12Val and 76% Gly13D; p: 0.60). In the Kaplan-Meier analysis, OS showed no difference between both kras mutation groups (Gly12Val: 29 months; Gly13D: 28 months; p: 0.28). Conclusions: According to our results, and in contrast to other studies outcomes, there is no difference regarding ORR, OCR or OS in patients with both different kras mutation: Gly12Val or Gly13D; however, the hypothetical role of Gly13D mutation as a biomarker of chemotherapy resistance, should be evaluated in prospective studies.

Next-generation sequencing to find predictors for chemotherapy response in triple-negative breast cancer (TNBC).

1010 Background: In TNBC initial response to chemotherapy is often favorable, but relapse and chemotherapy resistance frequently occur in advanced disease. Hence there is an urgent need for targeted treatments in this breast cancer subtype. Methods: To identify biomarkers of chemotherapy resistance and putative directed treatment targets, we performed next generation sequencing (NGS) of 2,000 genes implicated in oncogenesis. DNA from 31 pre-treatment biopsies and matched normal blood was sequenced. Biopsies were derived from patients scheduled to receive neoadjuvant chemotherapy with doxorubicin/cyclophosphamide. For the analysis, the tumors were divided in responders and non-responders, depending on whether or not a pathological complete remission (pCR) was achieved. Two definitions of pCR were employed: either the complete absence of infiltrating tumor cells in the breast (n=18) or a pCR of both the breast and lymph nodes (n=15). Tumors with partial or no responses were grouped in the non-responder category. Results: As a positive control, we verified that all patients with a known germline BRCA1 mutation (n=8) could be detected by the NGS analysis. In further analyses, we focused only on somatic mutations. Overall, the mutation rate was slightly higher in the non-responders (per tumor, average=12, range=[3-25]) than in the responders (average=8, range=[3-19]) (p=0.17). The analysis of individual genes did not reveal significant differences between responders and non-responders. However, pathway analysis showed that mutations in phosphatidylinositol signaling (e.g., PIK3CA, CALML5) were significantly more frequent in the non-responders, with mutations present in 10/13 non-responders and 2/18 responders (adjusted p=0.013). The chemokine and integrin signaling pathways also revealed a significantly higher mutation rate in the non-responders. Conclusions: Mutations in genes of the phosphatidylinositol pathway occur frequently in TNBCs that do not achieve a pCR on a neoadjuvant chemotherapy regimen consisting of doxorubicin and cyclophosphamide. After validation, alternative chemotherapy regimens and targeted agents should be investigated for these tumors.

PTMScan Direct: Identification and Quantification of Peptides from Critical Signaling Proteins by Immunoaffinity Enrichment Coupled with LC-MS/MS

Proteomic studies of post-translational modifications by metal affinity or antibody-based methods often employ data-dependent analysis, providing rich data sets that consist of randomly sampled identified peptides because of the dynamic response of the mass spectrometer. This can complicate the primary goal of programs for drug development, mutational analysis, and kinase profiling studies, which is to monitor how multiple nodes of known, critical signaling pathways are affected by a variety of treatment conditions. Cell Signaling Technology has developed an immunoaffinity-based LC-MS/MS method called PTMScan Direct for multiplexed analysis of these important signaling proteins. PTMScan Direct enables the identification and quantification of hundreds of peptides derived from specific proteins in signaling pathways or specific protein types. Cell lines, tissues, or xenografts can be used as starting material. PTMScan Direct is compatible with both SILAC and label-free quantification. Current PTMScan Direct reagents target key nodes of many signaling pathways (PTMScan Direct: Multipathway), serine/threonine kinases, tyrosine kinases, and the Akt/PI3K pathway. Validation of each reagent includes score filtering of MS/MS assignments, filtering by identification of peptides derived from expected targets, identification of peptides homologous to expected targets, minimum signal intensity of peptide ions, and dependence upon the presence of the reagent itself compared with a negative control. The Multipathway reagent was used to study sensitivity of human cancer cell lines to receptor tyrosine kinase inhibitors and showed consistent results with previously published studies. The Ser/Thr kinase reagent was used to compare relative levels of kinase-derived phosphopeptides in mouse liver, brain, and embryo, showing tissue-specific activity of many kinases including Akt and PKC family members. PTMScan Direct will be a powerful quantitative method for elucidation of changes in signaling in a wide array of experimental systems, combining the specificity of traditional biochemical methods with the high number of data points and dynamic range of proteomic methods.

P5-13-15: Proteomic Identification of Predictive Biomarkers of Resistance to Neoadjuvant Chemotherapy in Luminal Breast Cancer: A Possible Role for 14–3-3 and BID?

Abstract Background: Chemotherapy resistance is a major obstacle in effective neoadjuvant treatment for oestrogen receptor (ER)-positive breast cancer. The ability to predict tumour response would allow chemotherapy administration to be directed towards only those patients who would benefit, thus maximising treatment efficiency. We aimed to identify protein biomarkers associated with chemotherapy resistance, using proteomic analysis of fresh ER-positive breast cancer samples, and then to perform pilot clinical validation experiments. Materials and Methods: Chemotherapy resistant and chemotherapy sensitive tumour samples were collected from breast cancer patients who received standard anthracycline-based neoadjuvant therapy consisting of epirubicin with cyclophosphamide followed by docetaxel. Comparative proteomics experiments were performed using invasive ductal carcinomas which demonstrated ER-positivity (luminal subtype). Protein expression was compared between chemotherapy resistant and chemotherapy sensitive tumour samples using 2-dimensional gel electrophoresis (2-DE) with MALDI-TOF/TOF mass spectrometry (MS). In addition the Panorama XPRESS Profiler725 antibody microarray, containing 725 antibodies from a wide variety of cell signalling and apoptosis pathways, was employed in the discovery phase. Differentially expressed proteins (DEPs) were submitted to Ingenuity Pathway Analysis (IPA) to identify any canonical pathway links. A pilot series of archival breast cancer samples, from patients treated with neoadjuvant anthracycline-based chemotherapy, was used for preliminary clinical validation of putative predictive biomarkers. Results: Five datasets were generated by antibody microarray analysis, revealing 41 targets. Of these, 7 DEPs were identified in at least 2 datasets and these included 14–3-3, BID and Bcl-xL. The top canonical pathway matched in IPA was “ERK5 signaling”, which involved 6 DEPs, including 14–3-3. The “PI3/AKT” pathway also involved 6 DEPs, including 14–3-3 and Bcl-xL. Three datasets were generated using 2-DE with MALDI-TOF/TOF MS, containing over 300 DEPs. These included several isoforms of 14–3-3. Differential expression of 14–3-3, BID and Bcl-xL was confirmed by immunoblotting in samples used for the discovery phase. A pilot clinical validation using immunohistochemical analysis of archival breast cancers revealed 14–3-3 tau and tBID to be significantly associated with chemotherapy resistance. Discussion: We have successfully utilised clinical tumour samples for the discovery of putative biomarkers of chemotherapy resistance using two complementary proteomic platforms. We propose a potential role for 14–3-3 tau and BID as predictive biomarkers of chemotherapy resistance in ER-positive tumours and further validation in a larger sample series is now required. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P5-13-15.