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Posts Tagged ‘clonal heterogeneity’


Can IntraTumoral Heterogeneity Be Thought of as a Mechanism of Resistance?

Curator/Reporter: Stephen J. Williams, Ph.D.

Therapeutic resistance remains one of the most challenging problems for the oncologist, despite the increase of new therapeutics in the oncologist’s toolkit. As new targeted therapies are developed, and new novel targets are investigated as potential therapies, especially cytostatic therapies which it has become evident our understanding of chemoresistance is expanding beyond mechanisms to circumvent a drug’s pharmacologic mechanism of action (i.e. increased DNA repair and cisplatin) or pharmacokinetic changes (i.e. increased efflux by acquisition of a MDR phenotype).

In a talk at the 2015 AACR National Meeting, Dr. Charles Swanton discusses the development of tumor heterogeneity in the light of developing, or acquired, drug resistance. Chemoresistance is either categorized as acquired resistance (where resistance develops upon continued exposure to drug) or inherent resistance (related to a tumor being refractory or unresponsive to drug). Dr Swanton discusses findings where development of this heterogeneity (discussed here in a posting on Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing) and here (Notes On Tumor Heterogeneity: Targets and Mechanisms, from the 2015 AACR Meeting in Philadelphia PA) on recent findings on Branched Chain Heterogeneity) is resulting in clones resistant to the initial drug treatment.

To recount a bit of background I list the overall points of the one of previous posts on tumor heterogeneity (and an interview with Dr. Charles Swanton) are as follows:

Multiple biopsies of primary tumor and metastases are required to determine the full mutational landscape of a patient’s tumor

The intratumor heterogeneity will have an impact on the personalized therapy strategy for the clinician

Metastases arising from primary tumor clones will have a greater genomic instability and mutational spectrum than the tumor from which it originates

Tumors and their metastases do NOT evolve in a linear path but have a branched evolution and would complicate biomarker development and the prognostic and resistance outlook for the patient

 

The following is a curation of various talks and abstracts from the 2015 AACR National Meeting in Philadelphia on effects of clonal evolution and intratumoral heterogeneity of a tumor with respect to development of chemoresistance. As this theory of heterogeneity and clonal evolution is particularly new I attempted to present all works (although apologize for the length upfront) to forgo bias and so the reader may extract any information pertinent to their clinical efforts and research. However I will give a brief highlight summary below:

 

From the 2015 AACR National Meeting in Philadelphia

 

 

 

 

PresentationNumber:NGO2

Presentation Title: Polyclonal and heterogeneous resistance to targeted therapy in leukemia
Presentation Time: Monday, Apr 20, 2015, 10:40 AM -10:55 AM
Location: Room 201, Pennsylvania Convention Center
Author Block: Catherine C. Smith, Amy Paguirigan, Chen-Shan Chin, Michael Brown, Wendy Parker, Mark J. Levis, Alexander E. Perl, Kevin Travers, Corynn Kasap, Jerald P. Radich, Susan Branford, Neil P. Shah. University of California, San Francisco, CA, Fred Hutchinson Cancer Research Center, Seattle, WA, Pacific Biosciences, Menlo Park, CA, Royal Adelaide Hospital, Adelaide, Australia, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, University of California, San Francisco, CA
Abstract Body: Genomic studies in solid tumors have revealed significant branching intratumoral clonal genetic heterogeneity. Such complexity is not surprising in solid tumors, where sequencing studies have revealed thousands of mutations per tumor genome. However, in leukemia, the genetic landscape is considerably less complex. Chronic myeloid leukemia (CML) is the human malignancy most definitively linked to a single genetic lesion, the BCR-ABL gene fusion. Genome wide sequencing of acute myeloid leukemia (AML) has revealed that AML is the most genetically straightforward of all extensively sequenced adult cancers to date, with an average of 13 coding mutations and 3 or less clones identified per tumor.
In CML, tyrosine kinase inhibitors (TKIs) of BCR-ABL have resulted in high rates of remission. However, despite excellent initial response rates with TKI monotherapy, patients still relapse, including virtually all patients with Philadelphia-positive acute lymphoblastic leukemia and blast crisis CML. Studies of clinical resistance highlight BCR-ABL as the sole genetic driver in CML as secondary kinase domain (KD) mutations that prevent drug binding are the predominant mechanism of relapse on BCR-ABL TKIs.
In AML, a more diverse panel of disease-defining genetic mutations has been uncovered. However, in individual patients, a single oncogene can still drive disease. This is the case in FLT3 mutant AML, in which the investigational FLT3 TKI quizartinib achieved an initial response rate of ~50% in relapsed/refractory AML patients with activating FLT3 internal tandem duplication (ITD) mutations, though most patients eventually relapsed. Confirming the importance of FLT3 in disease maintenance, we showed that 8 of 8 patients who relapsed on quizartinib did so due to acquired drug-resistant FLT3 KD mutations.
Studies in CML have revealed that sequential TKI therapy is associated with additional complexity where multiple mutations can coexist separately in an individual patient (“polyclonality”) or in tandem on a single allele (“compound mutations”). In AML, we observed polyclonal FLT3-ITD KD mutations in 2 of 8 patients examined in our initial study of quizartinib resistance.
In light of the polyclonal KD mutations observed in CML and AML at the time of TKI relapse, we undertook next generation sequencing studies to determine the true genetic complexity in CML and AML patients at the time of relapse on targeted therapy. We used Pacific Biosciences RS Single Molecule Real Time (SMRT) third generation sequencing technology to sequence the entire ABL KD or the entire FLT3 juxtamembrane and KD on a single strand of DNA. Using this method, we assessed a total of 103 samples from 79 CML patients on ABL TKI therapy and 36 paired pre-treatment and relapse samples from 18 FLT3-ITD+ AML patients who responded to investigational FLT3 TKI therapy.
In CML, using SMRT sequencing, we detected all mutations previously detected by direct sequencing. Of samples in which multiple mutations were detectable by direct sequencing, 85% had compound mutant alleles detectable in a variety of combinations. Compound mutant alleles were comprised of both dominant and minor mutations, some which were not detectable by direct sequencing. In the most complex case, 12 individual mutant alleles comprised of 7 different mutations were identified in a single sample.
For 12 CML patients, we interrogated longitudinal samples (2-4 time points per patient) and observed complex clonal relationships with highly dynamic shifts in mutant allele populations over time. We detected compound mutations arising from ancestral single mutant clones as well as parallel evolution of de novo polyclonal and compound mutations largely in keeping with what would be expected to cause resistance to the second generation TKI therapy received by that patient.
We used a phospho-flow cytometric technique to assesses the phosphorylation status of the BCR-ABL substrate CRKL in as a method to test the ex vivo biochemical responsiveness of individual mutant cell populations to TKI therapy and assess functional cellular heterogeneity in a given patient at a given timepoint. Using this technique, we observed co-existing cell populations with differential ex vivo response to TKI in 2 cases with detectable polyclonal mutations. In a third case, we identified co-existence of an MLL-AF9 containing cell population that retained the ability to modulate p-CRKL in response to BCR-ABL TKIs along with a BCR-ABL containing only population that showed biochemical resistance to all TKIs, suggesting the co-existence of BCR-ABL independent and dependent resistance in a single patient.
In AML, using SMRT sequencing, we identified acquired quizartinib resistant KD mutations on the FLT3-ITD (ITD+) allele of 9 of 9 patients who relapsed after response to quizartinib and 4 of 9 patients who relapsed after response to the investigational FLT3 inhibitor, PLX3397. In 4 cases of quizartinib resistance and 3 cases of PLX3397 resistance, polyclonal mutations were observed, including 7 different KD mutations in one patient with PLX3397 resistance. In 7 quizartinib-resistant cases and 3 PLX3397-resistant cases, mutations occurred at the activation loop residue D835. When we examined non-ITD containing (ITD-) alleles, we surprisingly uncovered concurrent drug-resistant FLT3 KD mutations on ITD- alleles in 7 patients who developed quizartinib resistance and 4 patients with PLX3397 resistance. One additional PLX3397-resistant patient developed a D835Y mutation only in ITD- alleles at the time of resistance, suggesting selection for a non-ITD containing clone. All of the individual substitutions found on ITD- alleles were the same substitutions identified on ITD+ alleles for each individual patient.
Given that the same individual mutations found on ITD- alleles were also found on ITD+ alleles, we sought to determine whether these mutations were found in the same cell or were indicative of polyclonal blast populations in each patient. To answer this question, we performed single cell sorting of viably frozen blasts from 3 quizartinib-resistant patients with D835 mutations identified at the time of relapse and genotyped single cells for the presence or absence of ITD and D835 mutations. This analysis revealed striking genetic heterogeneity. In 2/3 cases, polyclonal D835 mutations were found in both ITD+ and ITD- cells. In all cases, FLT3-ITD and D835 mutations were found in both heterozygous and homozygous combinations. Most surprisingly, in all 3 patients, approximately 30-40% of FLT3-ITD+ cells had no identified quizartinib resistance-causing FLT3 KD mutation to account for resistance, suggesting the presence of non-FLT3 dependent resistance in all patients.
To determine that ITD+ cells lacking FLT3 KD mutations observed in patients relapsed on quizartinib are indeed consistent with leukemic blasts functionally resistant to quizartinib and do not instead represent a population of differentiated or non-proliferating cells, we utilized relapse blasts from another patient who initially achieved clearance of bone marrow blasts on quizartinib and developed a D835Y mutation at relapse. We performed a colony assay in the presence of 20nM quizartinib. As expected, this dose of quizartinib was unable to suppress the colony-forming ability of blasts from this relapsed patient when compared to DMSO treatment. Genotyping of individual colonies grown from this relapse sample in the presence of 20nM quizartinib again showed remarkable genetic heterogeneity, including ITD+ and ITD- colonies with D835Y mutations in homozygous and heterozygous combinations as well as ITD+ colonies without D835Y mutations, again suggesting the presence of blasts with non-FLT3 dependent resistance. Additionally, 4 colonies with no FLT3 mutations at all were identified in this sample, suggesting the presence of a quizartinib-resistant non-FLT3 mutant blast population. To see if we could identify specific mechanisms of off-target resistance, we performed targeted exome sequencing 33-AML relevant genes from relapse and pre-treatment DNA from all four patients and detected no new mutations in any genes other than FLT3 acquired at the time of disease relapse. Clonal genetic heterogeneity is not surprising in solid tumors, where multiple driver mutations frequently occur, but in CML and FLT3-ITD+ AML, where disease has been shown to be exquisitely dependent on oncogenic driver mutations, our studies suggest a surprising amount of clonal diversity. Our findings show that clinical TKI resistance in these diseases is amazingly intricate on the single allele level and frequently consists of both polyclonal and compound mutations that give rise to an complicated pool of TKI-resistant alleles that can change dynamically over time. In addition, we demonstrate that cell populations with off-target resistance can co-exist with other TKI-resistant populations, underscoring the emerging complexity of clinical TKI resistance. Such complexity argues strongly that monotherapy strategies in advanced CML and AML may be ultimately doomed to fail due to heterogeneous cell intrinsic resistance mechanisms. Ultimately, combination strategies that can address both on and off target resistance will be required to effect durable therapeutic responses.
Session Title: Tumor Heterogeneity and Evolution
Session Type: Educational Session
Session Start/End Time: Saturday, Apr 18, 2015, 1:00 PM – 3:00 PM
Location: Terrace Ballroom II-III (400 Level), Pennsylvania Convention Center
CME: CME-Designated
CME/CE Hours: 2
Session Description: One of the major challenges for both the measurement and management of cancer is its heterogeneity. Recent studies have revealed both extensive inter- and intra-tumor heterogeneity at the genotypic and phenotypic levels. Leaders in the field will discuss this challenge, its origins, dynamics and clinical importance. They will also review how we can best measure and deal with tumor heterogeneity, particularly intra-tumor heterogeneity.
Presentations:
Chairperson
Saturday, Apr 18, 2015, 1:00 PM – 3:00 PM
Carlo C. Maley. UCSF Helen Diller Family Comp. Cancer Center, San Francisco, CA
Universal biomarkers: How to handle tumor heterogeneity
Saturday, Apr 18, 2015, 1:00 PM – 1:25 PM
Carlo C. Maley. UCSF Helen Diller Family Comp. Cancer Center, San Francisco, CA
Discussion
Saturday, Apr 18, 2015, 1:25 PM – 1:30 PM
Heterogeneity of resistance to cancer therapy
Saturday, Apr 18, 2015, 1:30 PM – 1:55 PM
Ivana Bozic. HARVARD UNIV., Cambridge, MA
Discussion
Saturday, Apr 18, 2015, 1:55 PM – 2:00 PM
Determinants of phenotypic intra-tumor heterogeneity: integrative approach
Saturday, Apr 18, 2015, 2:00 PM – 2:25 PM
Andriy Marusyk, Michalina Janiszewska, Doris Tabassum. Dana-Farber Cancer Institute, Boston, MA, Dana-Farber Cancer Institute, Boston, MA
Discussion
Saturday, Apr 18, 2015, 2:25 PM – 2:30 PM
Cancer clonal complexity and evolution at the macro- and microheterogeneity scale
Saturday, Apr 18, 2015, 2:30 PM – 2:55 PM
Marco Gerlinger. Institute of Cancer Research, London, United Kingdom
Discussion
Saturday, Apr 18, 2015, 2:55 PM – 3:00 PM

From Ivana Bozic:

A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity.

Waclaw B, Bozic I, Pittman ME, Hruban RH, Vogelstein B, Nowak MA.

Nature. 2015 Sep 10;525(7568):261-4. doi: 10.1038/nature14971. Epub 2015 Aug 26.

PMID:

26308893

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Timing and heterogeneity of mutations associated with drug resistance in metastatic cancers.

Bozic I, Nowak MA.

Proc Natl Acad Sci U S A. 2014 Nov 11;111(45):15964-8. doi: 10.1073/pnas.1412075111. Epub 2014 Oct 27.

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Session Title: Mechanisms of Cancer Therapy Resistance
Session Type: Educational Session
Session Start/End Time: Saturday, Apr 18, 2015, 1:00 PM – 3:00 PM
Location: Room 204, Pennsylvania Convention Center
CME: CME-Designated
CME/CE Hours: 2
Session Description: Despite dramatic advances in the treatment of cancer, therapy resistance remains the most significant hurdle in improving the outcome of cancer patients. In this session, we will discuss many different aspects of therapy resistance, including a summary of our current understanding of therapy resistant tumor cell populations as well as analyses of the challenges associated with intratumoral heterogeneity and adaptive responses to targeted therapies.
Presentations:
Chairperson
Saturday, Apr 18, 2015, 1:00 PM – 3:00 PM
Charles Swanton. Cancer Research UK London Research Institute, London, United Kingdom
Tumor heterogeneity and drug resistance
Saturday, Apr 18, 2015, 1:00 PM – 1:30 PM
Charles Swanton. Cancer Research UK London Research Institute, London, United Kingdom
Discussion

Saturday, Apr 18, 2015, 1:30 PM – 1:40 PM
Discussion Discussion, Discussion

Principles of resistance to targeted therapy
Saturday, Apr 18, 2015, 1:40 PM – 2:10 PM
Levi A. Garraway. Dana-Farber Cancer Institute, Boston, MA
Discussion

Saturday, Apr 18, 2015, 2:10 PM – 2:20 PM
Discussion Discussion, Discussion

Adaptive re-wiring of signaling pathways driving drug resistance to targeted therapies
Saturday, Apr 18, 2015, 2:20 PM – 2:50 PM
Taru E. Muranen. Harvard Medical School, Boston, MA
Discussion

Saturday, Apr 18, 2015, 2:50 PM – 3:00 PM
Discussion Discussion, Discussion

Presentation Abstract  

 

 

 

Abstract Number: 737
Presentation Title: Clonal evolution of the HER2 L755S mutation as a mechanism of acquired HER-targeted therapy resistance
Presentation Time: Sunday, Apr 19, 2015, 1:00 PM – 5:00 PM
Location: Section 30
Poster Board Number: 29
Author Block: Xiaowei Xu1, Agostina Nardone1, Huizhong Hu1, Lanfang Qin1, Sarmistha Nanda1, Laura Heiser2, Nicholas Wang2, Kyle Covington1, Edward Chen1, Alexander Renwick1, Tamika Mitchell1, Marty Shea1, Tao Wang1, Carmine De Angelis1, Alejandro Contreras1, Carolina Gutierrez1, Suzanne Fuqua1, Gary Chamness1, Chad Shaw1, Marilyn Li1, David Wheeler1, Susan Hilsenbeck1, Mothaffar Fahed Rimawi1, Joe Gray2, C.Kent Osborne1, Rachel Schiff1. 1Baylor College of Medicine, Houston, TX; 2Oregon Health & Science University, Portland, OR
Abstract Body: Background: Targeting HER2 with lapatinib (L), trastuzumab (T), or the LT combination, is effective in HER2+ breast cancer (BC), but acquired resistance commonly occurs. In our 12-week neoadjuvant
trial (TBCRC006) of LT without chemotherapy in HER2+ BC, the overall pathologic complete response (pCR) rate was 27%. To investigate resistance mechanisms, we developed 10 HER2+ BC cell line
models resistant (R) to one or both drugs (LR/TR/LTR). To discover potential predictive markers/therapeutic targets to circumvent resistance, we completed genomic profiling of the cell lines and a
subset of pre-treatment specimens from TBCRC006.
Methods: Parental (P) and LR/TR/LTR lines of 10 cell line models were profiled with whole exome/RNA sequencing. Mutations detected in R lines but not in P lines of the same model were identified. Mutation-specific Q-PCR was designed for sensitive quantification. Resistant cell and xenograft tumor growth were measured in response to drugs. Whole exome sequencing (>100X) and Ampliseq of 17 baseline tumor/normal pairs from TBCRC006 were performed.
Results: We found and validated the HER2 L755S mutation in the BT474/ATCC-LTR line and BT474/AZ-LR line (in ~30% of DNA/RNA), in which the HER pathway was reactivated for resistance. Overexpression of this mutation was previously shown to induce LR in HER2-negative BC cell lines, and resistant growth of BT474/AZ-LR line is significantly inhibited by HER2-L755S-specific siRNA knock-down, suggesting its role as an acquired L/LT resistance driver in HER2+ BC. Sequencing of BT474/AZ-LR single cell clones found the mutation in ~30% of HER2 copies in every cell. Using mutation-specific Q-PCR, we found statistically higher HER2 L755S levels in two BT474 parentals compared to P lines of SKBR3, AU565, and UACC812. These data suggest that HER2 L755S resistant subclones preexist in the BT474 parentals and were selected by L treatment to become the major clone in the two R lines. The HER1/2 irreversible tyrosine kinase inhibitor (TKI) afatinib (Afa) robustly inhibited growth of BT474/AZ-LR and BT474/ATCC-LTR cells (IC50: Afa 0.02µM vs. L 3 µM) and BT474/AZ-LR xenografts. Whole exome sequencing/Ampliseq of TBCRC006 found the HER2 L755S mutation in 1/17 primaries. This patient did not achieve pCR. The variant was present in 2% of DNA on both platforms, indicating a subclonal event of the resistance mutation.
Conclusion: Acquired L/LT resistance in the two BT474 R lines is due to selection of HER2 L755S subclones present in parental cells. The higher HER2 L755S
levels in BT474 parentals compared with other parentals, and detection of its subclonal presence in a pre-treatment HER2+ BC patient, suggest that sensitive mutation detection methods will be needed to identify patients with potentially actionable HER family mutations in primary tumor. Treating this patient group
with an irreversible TKI like Afa may prevent resistance and improve clinical outcome of this subset of HER2+ BC.
Presentation Number: SY07-04
Presentation Title: The evolutionary landscape of CLL: Therapeutic implications
Presentation Time: Sunday, Apr 19, 2015, 2:25 PM – 2:45 PM
Location: Grand Ballroom (300 Level), Pennsylvania Convention Center
Author Block: Catherine J. Wu. Dana-Farber Cancer Institute, Boston, MA
Abstract Body: Clonal evolution is a key feature of cancer progression and relapse. Recent studies across cancers have demonstrated the extensive degree of intratumoral heterogeneity present within individual cancers. We hypothesized that evolutionary dynamics contribute to the variations in disease tempo and response to therapy that are highly characteristic of chronic lymphocytic leukemia (CLL). We have recently investigated this phenomenon by developing a pipeline that estimates the fraction of cancer cells harboring each somatic mutation within a tumor through integration of whole-exome sequence (WES) and local copy number data (Landau et al., Cell 2013). By applying this analysis approach to 149 CLL cases, we discovered earlier and later cancer drivers, uncovered patterns of clonal evolution in CLL and linked the presence of subclones harboring driver mutations with adverse clinical outcome. Thus, our study, generated from a heterogeneous sample cohort, strongly supports the concept that CLL clonal evolution arises from mass extinction and therapeutic bottlenecks which lead to the emergence of highly fit (and treatment resistant) subclones. We further hypothesized that epigenetic heterogeneity also shapes CLL clonal evolution through interrelation with genetic heterogeneity. Indeed, in recent work, we have uncovered stochastic methylation disorder as the primary cause of methylation changes in CLL and cancer in general, and that this phenomena impacts gene transcription, genetic evolution and clinical outcome. Thus, integrated studies of genetic and epigenetic heterogeneity in CLL have revealed the complex and diverse evolutionary trajectories of these cancer cells.
Immunotherapy is exquisitely suited for specifically and simultaneously targeting multiple lesions. We have developed an approach that leverages whole-exome sequencing to systematically identify personal tumor mutations with immunogenic potential, which can be incorporated as antigen targets in multi-epitope personalized therapeutic vaccines. We are pioneering this approach in an ongoing trial in melanoma and will now expand this concept to address diverse malignancies. Our expectation is that the choice of tumor neoantigens for a vaccine bypasses thymic tolerance and thus generates highly specific and potent high-affinity T cell responses to eliminate tumors in any cancer, including both ‘trunk’ and ‘branch’ lesions.

 

Abstract Number: LB-056
Presentation Title: TP53 and RB1 alterations promote reprogramming and antiandrogen resistance in advanced prostate cancer
Presentation Time: Sunday, Apr 19, 2015, 4:50 PM – 5:05 PM
Location: Room 122, Pennsylvania Convention Center
Author Block: Ping Mu, Zhen Cao, Elizabeth Hoover, John Wongvipat, Chun-Hao Huang, Wouter Karthaus, Wassim Abida, Elisa De Stanchina, Charles Sawyers. Memorial Sloan Kettering Cancer Center, New York, NY
Abstract Body: Castration-resistant prostate cancer (CRPC) is one of the most difficult cancers to treat with conventional methods and is responsible for nearly all prostate cancer deaths in the US. The Sawyers laboratory first showed that the primary mechanism of resistance to antiandrogen therapy is elevated androgen receptor (AR) expression. Research based on this finding has led to the development of next-generation antiandrogen: enzalutamide. Despite the exciting clinical success of enzalutamide, about 60% of patients exhibit various degrees of resistance to this agent. Highly variable responses to enzalutamide limit the clinical benefit of this novel antiandrogen, underscoring the importance of understanding the mechanisms of enzalutamide resistance. Most recently, an unbiased SU2C-Prostate Cancer Dream Team metastatic CRPC sequencing project led by Dr. Sawyers and Dr. Chinnaiyan revealed that mutations in the TP53 locus are the most significantly enriched alteration in CRPC tumors when compared to primary prostate cancers. Moreover, deletions and decreased expressions of the TP53 and RB1 loci (co-occurrence and individual occurrence) are more commonly associated with CRPC than with primary tumors. These results established that alteration of the TP53 and RB1 pathways are associated with the development of antiandrogen resistance.
By knockdowning TP53 or/and RB1 in the castration resistant LNCaP/AR model, we demonstrate that the disruption of either TP53 or RB1 alone confers significant resistance to enzalutamide both in vitro and in vivo. Strikingly, the co-inactivation of these pathways confers the most dramatic resistance. Since up-regulation of either AR or AR target genes is not observed in the resistant tumors, loss of TP53 and RB1 function confers enzalutamide resistance likely through an AR independent mechanism. In the clinic, resistance to enzalutamide is increasingly being associated with a transition to a poorly differentiated or neuroendocrine-like histology. Interestingly, we observed significant up-regulations of the basal cell marker Ck5 and the neuroendocrine-like cell marker Synaptophysin in the TP53 and RB1 inactivated cells, as well as down-regulation of the luminal cell marker Ck8. The differences between these markers became even greater after enzalutamide treatment. By using the p53-stabilizing drug Nutlin, level of p53 is rescued and consequently the the decrease of AR protein caused by RB1 and TP53 knockdown is reversed. These results strongly suggest that interference of TP53 and RB1 pathways confers antiandrogen resistance by “priming” prostate cancer cells to reprogramming or transdifferentiation, likely neuroendocrine-like differentiation, in response to treatment. Futher experiments will be performed to assess the molecular mechanism of TP53/RB1 alterations in mediating cell programming and conferring antiandrogen resistance.

 

Abstract Number: LB-146
Presentation Title: TGF-β-induced tumor heterogeneity and drug resistance of cancer stem cells
Presentation Time: Monday, Apr 20, 2015, 1:00 PM – 5:00 PM
Location: Section 41
Author Block: Naoki Oshimori1, Daniel Oristian1, Elaine Fuchs2. 1Rockefeller University, New York, NY; 2HHMI/Rockefeller University, New York, NY
Abstract Body: Among the most common and life-threatening cancers world-wide, squamous cell carcinoma (SCC) exhibit high rates of tumor recurrence following anti-cancer therapy. Subsets of cancer stem cells (CSCs) often escape anti-cancer therapeutics and promote recurrence. However, its sources and mechanisms that generate tumor heterogeneity and therapy-resistant cell population are largely unknown. Tumor microenvironment may drive intratumor heterogeneity by transmitting signaling factors, oxygen and metabolites to tumor cells depending on their proximity to the local sources. While the hypothesis is attractive, experimental evidence is lacking, and non-genetic mechanisms that drive functional heterogeneity remain largely unknown. As a potential non-genetic factor, we focused on TGF-β because of its multiple roles in tumor progression.
Here we devise a functional reporter system to monitor, track and modify TGF-β signaling in mouse skin SCC in vivo. Using this approach, we found that perivascular TGF-β in the tumor microenvironment generates heterogeneity in TGF-β signaling in neighboring CSCs. This heterogeneity is functionally important: small subsets of TGF-β-responding CSCs proliferate more slowly than their non-responding counterparts. They also exhibit invasive morphology and a malignant differentiation program compared to their non-responding neighbors. By lineage tracing, we show that although TGF-β-responding CSCs clonally expand more slowly they gain a growth advantage in a remarkable ability to escape cisplatin-induced apoptosis. We show that indeed it is their progenies that make a substantial contribution in tumor recurrence. Surprisingly, the slower proliferating state of this subset of CSCs within the cancer correlated with but did not confer the survival advantage to anti-cancer drugs. Using transcriptomic, biochemical and genetic analyses, we unravel a novel mechanism by which heterogeneity in the tumor microenvironment allows a subset of CSCs to respond to TGF-β, and evade anti-cancer drugs.
Our findings also show that TGF-β established ability to suppress proliferation and promote invasion and metastasis do not happen sequentially, but rather simultaneously. This new work build upon the roles of this factor in tumor progression, and sets an important paradigm for a non-genetic factor that produces tumor heterogeneity.
Abstract Number: LB-129
Presentation Title: Identifying tumor subpopulations and the functional consequences of intratumor heterogeneity using single-cell profiling of breast cancer patient-derived xenografts
Presentation Time: Monday, Apr 20, 2015, 1:00 PM – 5:00 PM
Location: Section 41
Author Block: Paul Savage1, Sadiq M. Saleh1, Ernesto Iacucci1, Timothe Revil1, Yu-Chang Wang1, Nicholas Bertos1, Anie Monast1, Hong Zhao1, Margarita Souleimanova1, Keith Szulwach2, Chandana Batchu2, Atilla Omeroglu1, Morag Park1, Ioannis Ragoussis1. 1McGill University, Montreal, QC, Canada; 2Fluidigm Corporation, South San Francisco, CA
Abstract Body: Human breast tumors have been shown to exhibit extensive inter- and intra-tumor heterogeneity. While recent advances in genomic technologies have allowed us to deconvolute this heterogeneity, few studies have addressed the functional consequences of diversity within tumor populations. Here, we identified an index case for which we have derived a patient-derived xenograft (PDX) as a renewable tissue source to identify subpopulations and perform functional assays. On pathology, the tumor was an invasive ductal carcinoma which was hormone receptor-negative, HER2-positive (IHC 2+, FISH average HER2/CEP17 2.4), though the FISH signal was noted to be heterogeneous. On gene expression profiling of bulk samples, the primary tumor and PDX were classified as basal-like. We performed single cell RNA and exome sequencing of the PDX to identify population structure. Using a single sample predictor of breast cancer subtype, we have identified single basal-like, HER2-enriched and normal-like cells co-existing within the PDX tumor. Genes differentially expressed between these subpopulations are involved in proliferation and differentiation. Functional studies distinguishing these subpopulations are ongoing. Microfluidic whole genome amplification followed by whole exome capture of 81 single cells showed high and homogeneous target enrichment with >75% of reads mapping uniquely on target. Variant calling using GATK and Samtools revealed founder mutations in key genes as BRCA1 and TP53, as well as subclonal mutations that are being investigated further. Loss of heterozygocity was observed in 16 TCGA cancer driver genes and novel mutations in 7 cancer driver genes. These findings may be important in understanding the functional consequences of intra-tumor heterogeneity with respect to clinically important phenotypes such as invasion, metastasis and drug-resistance.
Abstract Number: 2847
Presentation Title: High complexity barcoding to study clonal dynamics in response to cancer therapy
Presentation Time: Monday, Apr 20, 2015, 4:35 PM – 4:50 PM
Location: Room 118, Pennsylvania Convention Center
Author Block: Hyo-eun C. Bhang1, David A. Ruddy1, Viveksagar Krishnamurthy Radhakrishna1, Rui Zhao2, Iris Kao1, Daniel Rakiec1, Pamela Shaw1, Marissa Balak1, Justina X. Caushi1, Elizabeth Ackley1, Nicholas Keen1, Michael R. Schlabach1, Michael Palmer1, William R. Sellers1, Franziska Michor2, Vesselina G. Cooke1, Joshua M. Korn1, Frank Stegmeier1. 1Novartis Institutes for BioMedical Research, Cambridge, MA; 2Dana-Farber Cancer Institute, Boston, MA
Abstract Body: Targeted therapies, such as erlotinib and imatinib, lead to dramatic clinical responses, but the emergence of resistance presents a significant challenge. Recent studies have revealed intratumoral heterogeneity as a potential source for the emergence of therapeutic resistance. However, it is still unclear if relapse/resistance is driven predominantly by pre-existing or de novo acquired alterations. To address this question, we developed a high-complexity barcode library, ClonTracer, which contains over 27 million unique DNA barcodes and thus enables the high resolution tracking of cancer cells under drug treatment. Using this library in two clinically relevant resistance models, we demonstrate that the majority of resistant clones pre-exist as rare subpopulations that become selected in response to therapeutic challenge. Furthermore, our data provide direct evidence that both genetic and non-genetic resistance mechanisms pre-exist in cancer cell populations. The ClonTracer barcoding strategy, together with mathematical modeling, enabled us to quantitatively dissect the frequency of drug-resistant subpopulations and evaluate the impact of combination treatments on the clonal complexity of these cancer models. Hence, monitoring of clonal diversity in drug-resistant cell populations by the ClonTracer barcoding strategy described here may provide a valuable tool to optimize therapeutic regimens towards the goal of curative cancer therapies.
Abstract Number: 3590
Presentation Title: Resistance mechanisms to ALK inhibitors
Presentation Time: Tuesday, Apr 21, 2015, 8:00 AM -12:00 PM
Location: Section 31
Poster Board Number: 13
Author Block: Ryohei Katayama1, Noriko Yanagitani1, Sumie Koike1, Takuya Sakashita1, Satoru Kitazono1, Makoto Nishio1, Yasushi Okuno2, Jeffrey A. Engelman3, Alice T. Shaw3, Naoya Fujita1. 1Japanese Foundation for Cancer Research, Tokyo, Japan; 2Graduate School of Medicine, Kyoto University, Kyoto, Japan; 3Massachusetts General Hospital Cancer Center, Boston, MA
Abstract Body: Purpose: ALK-rearranged non-small cell lung cancer (NSCLC) was first reported in 2007. Approximately 3-5% of NSCLCs harbor an ALK gene rearrangement. The first-generation ALK tyrosine kinase inhibitor (TKI) crizotinib is a standard therapy for patients with advanced ALK-rearranged NSCLC. Several next-generation ALK-TKIs have entered the clinic and have shown promising antitumor activity in crizotinib-resistant patients. As patients still relapse even on these next-generation ALK-TKIs, we examined mechanisms of resistance to one next-generation ALK-TKI – alectinib – and potential strategies to overcome this resistance.
Experimental Procedure: We established a cell line model of alectinib resistance, and analyzed resistant tumor specimens from patients who had relapsed on alectinib. Cell lines were also established under an IRB-approved protocol when there was sufficient fresh tumor tissue. We established Ba/F3 cells expressing EML4-ALK and performed ENU mutagenesis to compare potential crizotinib or alectinib-resistance mutations. In addition, we developed Ba/F3 models harboring ALK resistance mutations and evaluated the potency of multiple next-generation ALK-TKIs including 3rd generation ALK inhibitor in these models and in vivo. To elucidate structure-activity-relationships of ALK resistance mutations, we performed computational thermodynamic simulation with MP-CAFEE.
Results: We identified multiple resistance mutations, including ALK I1171N, I1171S, and V1180L, from the ENU mutagenesis screen and the cell line model. In addition we found secondary mutations at the I1171 residue from the Japanese patients who developed resistance to alectinib or crizotinib. Both ALK mutations (V1180L and I1171 mutations) conferred resistance to alectinib as well as to crizotinib, but were sensitive to ceritinib and other next-generation ALK-TKIs. Based on thermodynamics simulation, each resistance mutation is predicted to lead to distinct structural alterations that decrease the binding affinity of ALK-TKIs for ALK.
Conclusions: We have identified multiple alectinib-resistance mutations from the cell line model, patient derived cell lines, and tumor tissues, and ENU mutagenesis. ALK secondary mutations arising after alectinib exposure are sensitive to other next generation ALK-TKIs. These findings suggest a potential role for sequential therapy with multiple next-generation ALK-TKIs in patients with advanced, ALK-rearranged cancers.
Session Title: Mechanisms of Resistance: From Signaling Pathways to Stem Cells
Session Type: Major Symposium
Session Start/End Time: Tuesday, Apr 21, 2015, 10:30 AM -12:30 PM
Location: Terrace Ballroom II-III (400 Level), Pennsylvania Convention Center
CME: CME-Designated
CME/CE Hours: 2
Session Description: Even the most effective cancer therapies are limited due to the development of one or more resistance mechanisms. Acquired resistance to targeted therapies can, in some cases, be attributed to the selective propagation of a small population of intrinsically resistant cells. However, there is also evidence that cancer drugs themselves can drive resistance by triggering the biochemical- or genetic-reprogramming of cells within the tumor or its microenvironment. Therefore, understanding drug resistance at the molecular and biological levels may enable the selection of specific drug combinations to counteract these adaptive responses. This symposium will explore some of the recent advances addressing the molecular basis of cancer cell drug resistance. We will address how tumor cell signaling pathways become rewired to facilitate tumor cell survival in the face of some of our most promising cancer drugs. Another topic to be discussed involves how drugs select for or induce the reprogramming of tumor cells toward a stem-like, drug resistant fate. By targeting the molecular driver(s) of rewired signaling pathways and/or cancer stemness it may be possible to select drug combinations that prevent the reprogramming of tumors and thereby delay or eliminate the onset of drug resistance.
Presentations:
Chairperson
Tuesday, Apr 21, 2015, 10:30 AM -12:30 PM
David A. Cheresh. UCSD Moores Cancer Center, La Jolla, CA
Introduction
Tuesday, Apr 21, 2015, 10:30 AM -10:40 AM
Resistance to tyrosine kinase inhibitors: Heterogeneity and therapeutic strategies.
Tuesday, Apr 21, 2015, 10:40 AM -10:55 AM
Jeffrey A. Engelman. Massachusetts General Hospital, Boston, MA
Discussion
Tuesday, Apr 21, 2015, 10:55 AM -11:00 AM
NG04: Clinical acquired resistance to RAF inhibitor combinations in BRAF mutant colorectal cancer through MAPK pathway alterations
Tuesday, Apr 21, 2015, 11:00 AM -11:15 AM
Ryan B. Corcoran, Leanne G. Ahronian, Eliezer Van Allen, Erin M. Coffee, Nikhil Wagle, Eunice L. Kwak, Jason E. Faris, A. John Iafrate, Levi A. Garraway, Jeffrey A. Engelman. Massachusetts General Hospital Cancer Center, Boston, MA, Dana-Farber Cancer Institute, Boston, MA
Discussion
Tuesday, Apr 21, 2015, 11:15 AM -11:20 AM
SY27-02: Tumour heterogeneity and therapy resistance in melanoma
Tuesday, Apr 21, 2015, 11:20 AM -11:35 AM
Claudia Wellbrock. Univ. of Manchester, Manchester, United Kingdom

Presentation Number: SY27-02
Presentation Title: Tumour heterogeneity and therapy resistance in melanoma
Presentation Time: Tuesday, Apr 21, 2015, 11:20 AM -11:35 AM
Location: Terrace Ballroom II-III (400 Level), Pennsylvania Convention Center
Author Block: Claudia Wellbrock. Univ. of Manchester, Manchester, United Kingdom
Abstract Body: Solid tumors are structurally very complex; they consist of heterogeneous cancer cell populations, other non-cancerous cell types and a distinct extracellular matrix. Interactions of cancer cells with non-cancerous cells is well investigated, and our recent work in melanoma has demonstrated that the cellular environment that surrounds cancer cells has a major impact on the way a patient responds to MAP-kinase pathway targeting therapy.
We have shown that intra-tumor signaling within a heterogeneous tumor can have a major impact on the efficacy of BRAF and MEK inhibitors. With the increasing evidence of genetic and phenotypic heterogeneity within tumors, intra-tumor signaling between individual cancer-cell subpopulations is therefore a crucial factor that needs to be considered in future therapy approaches. Our work has identified the ‘melanocyte-lineage survival oncogene’ MITF as an important player in phenotypic heterogeneity (MITFhigh and MITFlow cells) in melanoma, and MITF expression levels are crucial for the response to MAP-kinase pathway targeted therapy. We found that ‘MITF heterogeneity’ can be caused by cell-autonomous mechanisms or by the microenvironment, including the immune-microenvironment.
We have identified various mechanisms underlying MITF action in resistance to BRAF and MEK inhibitors in melanoma. In MITFhigh expressing cells, MITF confers cell-autonomous resistance to MAP-kinase pathway targeted therapy. Moreover, it appears that in melanomas heterogeneous for MITF expression (MITFhigh and MITFlow cells), individual subpopulations of resistant and sensitive cells communicate and MITF can contribute to overall tumor-resistance through intra-tumor signaling. Finally, we have identified a novel approach of interfering with MITF action, which profoundly sensitizes melanoma to MAP-kinase pathway targeted therapy.
Discussion
Tuesday, Apr 21, 2015, 11:35 AM -11:40 AM
SY27-03: Breast cancer stem cell state transitions mediate therapeutic resistance
Tuesday, Apr 21, 2015, 11:40 AM -11:55 AM
Max S. Wicha. University of Michigan, Comprehensive Cancer Center, Ann Arbor, MI
Discussion
Tuesday, Apr 21, 2015, 11:55 AM -12:00 PM
SY27-04: Induction of cancer stemness and drug resistance by EGFR blockade
Tuesday, Apr 21, 2015, 12:00 PM -12:15 PM
David A. Cheresh. UCSD Moores Cancer Center, La Jolla, CA

 

Cellular Reprogramming in Carcinogenesis: Implications for Tumor Heterogeneity, Prognosis, and Therapy
Session Type: Major Symposium
Session Start/End Time: Tuesday, Apr 21, 2015, 10:30 AM -12:30 PM
Location: Room 103, Pennsylvania Convention Center
CME: CME-Designated
CME/CE Hours: 2
Session Description: Cancers, both solid and liquid, consist of phenotypically heterogeneous cell types that make up the full cellular complement of disease. Deep sequencing of bulk cancers also frequently reveals a genetic intratumoral heterogeneity that reflects clonal evolution in space and in time and under the influence of treatment. How the distinct phenotypic and genotypic cells contribute to individual cancer growth and progression is incompletely understood. In this symposium, we will discuss issues of cancer heterogeneity and effects on growth and treatment resistance, with emphasis on cancer cell functional properties and influences of the microenvironment, interclonal genomic heterogeneity, and lineage relationships between cancer cells with stem cell and differentiated properties. Understanding these complex cellular relationships within cancers will have critical implications for devising more effective treatments.
Presentations:
Chairperson
Tuesday, Apr 21, 2015, 10:30 AM -12:30 PM
Peter B. Dirks. Univ. of Toronto Hospital for Sick Children, Toronto, ON, Canada
Introduction

Tuesday, Apr 21, 2015, 10:30 AM -10:40 AM

Origins, evolution and selection in childhood leukaemia
Tuesday, Apr 21, 2015, 10:40 AM -11:00 AM
Tariq Enver. Cancer Research UK, London, United Kingdom
Discussion

Tuesday, Apr 21, 2015, 11:00 AM -11:05 AM

Cytokine-controlled stem cell plasticity inintestinal tumorigenesis
Tuesday, Apr 21, 2015, 11:05 AM -11:25 AM
Florian Greten. Georg-Speyer-Haus, Frankfurt, Germany
Discussion

Tuesday, Apr 21, 2015, 11:25 AM -11:30 AM

SY23-03: Intratumoural heterogeneity in human serous ovarian carcinoma
Tuesday, Apr 21, 2015, 11:30 AM -11:50 AM
John P. Stingl. Cancer Research UK Cambridge Research Inst., Cambridge, United Kingdom
Discussion

Tuesday, Apr 21, 2015, 11:50 AM -11:55 AM

Functional and genomic heterogeneity in brain tumors
Tuesday, Apr 21, 2015, 11:55 AM -12:15 PM

 

Proc Natl Acad Sci U S A. 2015 Jan 20;112(3):851-6. doi: 10.1073/pnas.1320611111. Epub 2015 Jan 5.

Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity.

Meyer M1, Reimand J2, Lan X3, Head R1, Zhu X1, Kushida M1, Bayani J4, Pressey JC5, Lionel AC6, Clarke ID7, Cusimano M8, Squire JA9, Scherer SW6, Bernstein M10, Woodin MA5, Bader GD11, Dirks PB12.

Author information

Abstract

Glioblastoma (GBM) is a cancer comprised of morphologically, genetically, and phenotypically diverse cells. However, an understanding of the functional significance of intratumoral heterogeneity is lacking. We devised a method to isolate and functionally profile tumorigenic clones from patient glioblastoma samples. Individual clones demonstrated unique proliferation and differentiation abilities. Importantly, naïve patient tumors included clones that were temozolomide resistant, indicating that resistance to conventional GBM therapy can preexist in untreated tumors at a clonal level. Further, candidate therapies for resistant clones were detected with clone-specific drug screening. Genomic analyses revealed genes and pathways that associate with specific functional behavior of single clones. Our results suggest that functional clonal profiling used to identify tumorigenic and drug-resistant tumor clones will lead to the discovery of new GBM clone-specific treatment strategies.

—————————————————————————————————

 

739: Tumor cell plasticity with transition to a mesenchymal phenotype is a mechanism of chemoresistance that is reversed by Notch pathway inhibition in lung adenocarcinoma
Sunday, Apr 19, 2015, 1:00 PM – 5:00 PM
Khaled A. Hassan. University Of Michigan, Ann Arbor, MI

745: Oncostatin M receptor activation leads to molecular targeted therapy resistance in non-small cell lung cancer
Sunday, Apr 19, 2015, 1:00 PM – 5:00 PM
Kazuhiko Shien1, Vassiliki A. Papadimitrakopoulou1, Dennis Ruder1, Nana E. Hanson1, Neda Kalhor1, J. Jack Lee1, Waun Ki Hong1, Ximing Tang1, Roy S. Herbst2, Luc Girard3, John D. Minna3, Jonathan M. Kurie1, Ignacio I. Wistuba1, Julie G. Izzo1. 1University of Texas MD Anderson Cancer Center, Houston, TX; 2Yale Cancer Center, Yale School of Medicine, New Haven, CT; 3Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, TX

746: Activation of EGFR bypass signaling through TGFα overexpression induces acquired resistance to alectinib in ALK-translocated lung cancer cells
Sunday, Apr 19, 2015, 1:00 PM – 5:00 PM
Tetsuo Tani, Hiroyuki Yasuda, Junko Hamamoto, Aoi Kuroda, Daisuke Arai, Kota Ishioka, Keiko Ohgino, Ichiro Kawada, Katsuhiko Naoki, Hayashi Yuichiro, Tomoko Betsuyaku, Kenzo Soejima. Keio University, Tokyo, Japan

752: Elucidating the mechanisms of acquired resistance in lung adenocarcinomas
Sunday, Apr 19, 2015, 1:00 PM – 5:00 PM
Sandra Ortiz-Cuarán1, Lynnette Fernandez-Cuesta1, Christine M. Lovly2, Marc Bos1, Matthias Scheffler3, Sebastian Michels3, Kerstin Albus4, Lydia Meyer4, Katharina König4, Ilona Dahmen1, Christian Mueller1, Luca Ozretić4, Lars Tharun4, Philipp Schaub1, Alexandra Florin4, Berit Pinther1, Nike Bahlmann1, Sascha Ansén3, Martin Peifer1, Lukas C. Heukamp4, Reinhard Buettner4, Martin L. Sos1, Jürgen Wolf3, William Pao2, Roman K. Thomas1. 1University of Cologne, Cologne, Germany; 2Department of Medicine, Vanderbilt University, Nashville, TN; 3Department of Internal Medicine, Center for Integrated Oncology Köln-Bonn, University Hospital Cologne, Cologne, Germany; 4Institute of Pathology, University Hospital Cologne, Cologne, Germany

760: On the evolution of erlotinib-resistant NSCLC subpopulations
Sunday, Apr 19, 2015, 1:00 PM – 5:00 PM
Michael E. Ramirez1, Robert J. Steininger, III1, Lani F. Wu2, Steven J. Altschuler2. 1UT Southwestern, Dallas, TX; 2UCSF, San Francisco, CA
763: Implications of resistance patterns with NSCLC targeted agents
Sunday, Apr 19, 2015, 1:00 PM – 5:00 PM
David J. Stewart, Paul Wheatley-Price, Rob MacRae, Jason Pantarotto. University of Ottawa, Ottawa, ON, Canada

 

768: A kinome-wide siRNA screen identifies modifiers of sensitivity to the EGFR T790M-targeted tyrosine kinase inhibitor (TKI), AZD9291, in EGFR mutant lung adenocarcinoma
Sunday, Apr 19, 2015, 1:00 PM – 5:00 PM
Eiki Ichihara1, Joshua A. Bauer2, Pengcheng Lu3, Fei Ye3, Darren Cross4, William Pao1, Christine M. Lovly1. 1Vanderbilt University School of Medicine, Nashville, TN; 2Vanderbilt Institute of Chemical Biology High-Throughput Screening Facility, Nashville, TN; 3Vanderbilt University Medical Center, Nashville, TN; 4AstraZeneca Oncology Innovative Medicines, United Kingdom

LB-055: Clinical acquired resistance to RAF inhibitor combinations in BRAF-mutant colorectal cancer through MAPK pathway alterations
Sunday, Apr 19, 2015, 4:35 PM – 4:50 PM
Leanne G. Ahronian1, Erin M. Sennott1, Eliezer M. Van Allen2, Nikhil Wagle2, Eunice L. Kwak1, Jason E. Faris1, Jason T. Godfrey1, Koki Nishimura1, Kerry D. Lynch3, Craig H. Mermel1, Elizabeth L. Lockerman1, Anuj Kalsy1, Joseph M. Gurski, Jr.1, Samira Bahl4, Kristin Anderka4, Lisa M. Green4, Niall J. Lennon4, Tiffany G. Huynh3, Mari Mino-Kenudson3, Gad Getz1, Dora Dias-Santagata3, A. John Iafrate3, Jeffrey A. Engelman1, Levi A. Garraway2, Ryan B. Corcoran1. 1Massachusetts General Hospital Cancer Center, Boston, MA; 2Dana Farber Cancer Institute, Boston, MA; 3Massachusetts General Hospital Department of Pathology, Boston, MA; 4Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA

 

Other Articles on this Site Related to Tumor Heterogeneity Include

Notes On Tumor Heterogeneity: Targets and Mechanisms, from the 2015 AACR Meeting in Philadelphia PA

Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

CANCER COMPLEXITY: Heterogeneity in Tumor Progression and Drug Response – 2015 Annual Symposium @Koch Institute for Integrative Cancer Research at MIT – W34, 6/12/2015 9:00 AM EDT – 4:30 PM EDT

In vitro Models of Tumor Microenvironment for New Cancer Target and Drug Discovery, 11/17 – 11/19/2014, Hyatt Boston Harbor

What can we expect of tumor therapeutic response?

 

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Notes On Tumor Heterogeneity: Targets and Mechanisms, from the 2015 AACR Meeting in Philadelphia PA

Reporter: Stephen J. Williams, Ph.D.

The following contain notes from the Sunday April 19, 2015 AACR Meeting (Pennsylvania Convention Center, Philadelphia PA) 1 PM Major Symposium Session on Tumor Heterogeneity: Targets and Mechanism chaired by Dr. Charles Swanton.

Speakers included: Mark J. Smyth, Charles Swanton, René H. Medema, and Catherine J. Wu

Tumor heterogeneity is a common feature of many malignancies, especially the solid tumors and can drive the evolution and adaptation of the growing tumor, complicating therapy and resulting in therapeutic failure, including resistance. This session at AACR described the mechanisms, both genetic and epigenetic, which precipitate intratumor heterogeneity and how mutational processes and chromosomal instability may impact the tumor progression and the origin of driver events during tumor evolution. Finally the session examined possible therapeutic strategies to take advantage of, and overcome, tumor evolution. The session was chaired by Dr. Charles Swanton. For a more complete description of his work, tumor heterogeneity, and an interview on this site please click on the link below:

Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

and

Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

 

Notes from Charles Swanton, Cancer Research UK; Identifying Drivers of Cancer Diversity

Dr. Swanton’s lecture focused on data from two recent papers from his lab by Franseco Favero and Nicholas McGranahan:

  1. Glioblastoma adaptation Traced Through Decline of an IDH1 clonal driver and macro-evolution of a double-minute chromosome (Annals of Oncology, 2015)[1]

This paper described the longitudinal Whole Genome Sequencing (WGS) study of a 35 year old female whose primary glioblastoma (GBM) was followed through temozolomide treatment and ultimately recurrence.

  • In 2008 patient was diagnosed with primary GBM (three biopsies of unrelated sites were Grade II and Grade IV; temozolomide therapy for three years then relapse in 2011
  • WGS of 2 areas of primary tumor showed extensive mutational and copy number heterogeneity; was able to identify clonal TP53 mutations and clonal IDH1 mutation in primary tumor with different patterns of clonality based on grade
  • Amplifications on chromosome 4 and 12 (PDGFRA, KIT, CDK4)
  • After three years of temozolomide multiple translocations found in chromosome 4 and 12 (6 translocations)
  • Clonal IDH1 R132H mutation in primary tumor only at very low frequency in recurrent tumor
  • The WGS on recurrent tumor (sequencing took ONLY 9 days from tumor resection to sequence results) showed mutation cluster in KIT/PDGFRA.PI3K.mTOR axis so patient treated with imatinib
  • However despite rapid sequencing and a personalized approach based on WGS results, tumor progressed and patient died shortly: tumor evolution is HUGE hurdle for personalized medicine

As Dr. Swanton stated:

“we are underestimating the frequency of polyclonal evolution”

  1. Clonal status of actionable driver events and the timing of mutational processes in cancer evolution (Science Translational Medicine, 2015)[2]
  • analyzed nine cancer types to determine the subclonal frequencies of driver events, to time mutational processes during cancer evolution, and to identify drivers of subclonal expansions.
  • identified later subclonal “actionable” mutations, including BRAF (V600E), IDH1 (R132H), PIK3CA (E545K), EGFR (L858R), and KRAS (G12D), which may compromise the efficacy of targeted therapy approaches.
  • > 20% of IDH1 mutations in glioblastomas, and 15% of mutations in genes in the PI3K (phosphatidylinositol 3-kinase)–AKT–mTOR (mammalian target of rapamycin) signaling axis across all tumor types were subclonal
  • Mutations in the RAS–MEK (mitogen-activated protein kinase kinase) signaling axis were less likely to be subclonal than mutations in genes associated with PI3K-AKT-mTOR signaling

Branched chain can converge on single resistance mechanism; clonal resistance (for example to PI3K inhibitors can get multiple PTEN mutations in various metastases

Targeting Tumor Heterogeneity

  • Identify high risk occupants (have to know case history)
  • Mutational landscape interferes with anti-PD1 therapies
  • Low frequency mutations affect outcome

Notes from Dr. Catherine J. Wu, Dana-Farber Cancer Institute: The evolutionary landscape of CLL: Therapeutic implications

  • Clonal evolution a key feature of cancer progression and relapse
  • Hypothesis: evolutionary dynamics (heterogeneity) in chronic lymphocytic leukemia (CLL) contributes to variations in response and disease “tempo”
  • Used whole exome sequencing and copy number data of 149 CLL cases to discover early and late cancer drivers: clonal patterns (Landau et. al, Cell 2013); some drivers correspond to poor clinical outcome
  • Methylation studies suggest that there is epigenetic heterogeneity which may drive CLL clonal evolution
  • Developing methodology to integrate WES to determine mutations with immunogenic potential for development of personalized immunotherapy for CLL and other malignancies

References

  1. Favero F, McGranahan N, Salm M, Birkbak NJ, Sanborn JZ, Benz SC, Becq J, Peden JF, Kingsbury Z, Grocok RJ et al: Glioblastoma adaptation traced through decline of an IDH1 clonal driver and macro-evolution of a double-minute chromosome. Annals of oncology : official journal of the European Society for Medical Oncology / ESMO 2015, 26(5):880-887.
  2. McGranahan N, Favero F, de Bruin EC, Birkbak NJ, Szallasi Z, Swanton C: Clonal status of actionable driver events and the timing of mutational processes in cancer evolution. Science translational medicine 2015, 7(283):283ra254.

 

Other related articles on Tumor Heterogeneity were published in this Open Access Online Scientific Journal, include the following:

 

Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

CANCER COMPLEXITY: Heterogeneity in Tumor Progression and Drug Response – 2015 Annual Symposium @Koch Institute for Integrative Cancer Research at MIT – W34, 6/12/2015 9:00 AM EDT – 4:30 PM EDT

My Cancer Genome from Vanderbilt University: Matching Tumor Mutations to Therapies & Clinical Trials

Tumor Imaging and Targeting: Predicting Tumor Response to Treatment: Where we stand?

Mitochondrial Isocitrate Dehydrogenase and Variants

War on Cancer Needs to Refocus to Stay Ahead of Disease Says Cancer Expert

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Genetic Analysis of Atrial Fibrillation

Author and Curator: Larry H Bernstein, MD, FCAP  

and 

Curator: Aviva-Lev Ari, PhD, RN

This article is a followup of the wonderful study of the effect of oxidation of a methionine residue in calcium dependent-calmodulin kinase Ox-CaMKII on stabilizing the atrial cardiomyocyte, giving protection from atrial fibrillation.  It is also not so distant from the work reviewed, mostly on the ventricular myocyte and the calcium signaling by initiation of the ryanodyne receptor (RyR2) in calcium sparks and the CaMKII d isoenzyme.

We refer to the following related articles published in pharmaceutical Intelligence:

Oxidized Calcium Calmodulin Kinase and Atrial Fibrillation
Author: Larry H. Bernstein, MD, FCAP and Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/10/26/oxidized-calcium-calmodulin-kinase-and-atrial-fibrillation/

Jmjd3 and Cardiovascular Differentiation of Embryonic Stem Cells

Author: Larry H. Bernstein, MD, FCAP and Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/10/26/jmjd3-and-cardiovascular-differentiation-of-embryonic-stem-cells/

Contributions to cardiomyocyte interactions and signaling
Author and Curator: Larry H Bernstein, MD, FCAP  and Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/10/21/contributions-to-cardiomyocyte-interactions-and-signaling/

Cardiac Contractility & Myocardium Performance: Therapeutic Implications for Ryanopathy (Calcium Release-related Contractile Dysfunction) and Catecholamine Responses
Editor: Justin Pearlman, MD, PhD, FACC, Author and Curator: Larry H Bernstein, MD, FCAP, and Article Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/08/28/cardiac-contractility-myocardium-performance-ventricular-arrhythmias-and-non-ischemic-heart-failure-therapeutic-implications-for-cardiomyocyte-ryanopathy-calcium-release-related-contractile/

Part I. Identification of Biomarkers that are Related to the Actin Cytoskeleton
Curator and Writer: Larry H Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2012/12/10/identification-of-biomarkers-that-are-related-to-the-actin-cytoskeleton/

Part II: Role of Calcium, the Actin Skeleton, and Lipid Structures in Signaling and Cell Motility
Larry H. Bernstein, MD, FCAP, Stephen Williams, PhD and Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/08/26/role-of-calcium-the-actin-skeleton-and-lipid-structures-in-signaling-and-cell-motility/

Part IV: The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets
Larry H Bernstein, MD, FCAP, Justin Pearlman, MD, PhD, FACC and Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/09/08/the-centrality-of-ca2-signaling-and-cytoskeleton-involving-calmodulin-kinases-and-ryanodine-receptors-in-cardiac-failure-arterial-smooth-muscle-post-ischemic-arrhythmia-similarities-and-differen/

Part VI: Calcium Cycling (ATPase Pump) in Cardiac Gene Therapy: Inhalable Gene Therapy for Pulmonary Arterial Hypertension and Percutaneous Intra-coronary Artery Infusion for Heart Failure: Contributions by Roger J. Hajjar, MD
Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/08/01/calcium-molecule-in-cardiac-gene-therapy-inhalable-gene-therapy-for-pulmonary-arterial-hypertension-and-percutaneous-intra-coronary-artery-infusion-for-heart-failure-contributions-by-roger-j-hajjar/

Part VII: Cardiac Contractility & Myocardium Performance: Ventricular Arrhythmias and Non-ischemic Heart Failure – Therapeutic Implications for Cardiomyocyte Ryanopathy (Calcium Release-related Contractile Dysfunction) and Catecholamine Responses
Justin Pearlman, MD, PhD, FACC, Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/08/28/cardiac-contractility-myocardium-performance-ventricular-arrhythmias-and-non-ischemic-heart-failure-therapeutic-implications-for-cardiomyocyte-ryanopathy-calcium-release-related-contractile/

Part VIII: Disruption of Calcium Homeostasis: Cardiomyocytes and Vascular Smooth Muscle Cells: The Cardiac and Cardiovascular Calcium Signaling Mechanism
Justin Pearlman, MD, PhD, FACC, Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/09/12/disruption-of-calcium-homeostasis-cardiomyocytes-and-vascular-smooth-muscle-cells-the-cardiac-and-cardiovascular-calcium-signaling-mechanism/

Part IX: Calcium-Channel Blockers, Calcium Release-related Contractile Dysfunction (Ryanopathy) and Calcium as Neurotransmitter Sensor
Justin Pearlman, MD, PhD, FACC, Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/09/16/calcium-channel-blocker-calcium-as-neurotransmitter-sensor-and-calcium-release-related-contractile-dysfunction-ryanopathy/

Part X: Synaptotagmin functions as a Calcium Sensor: How Calcium Ions Regulate the fusion of vesicles with cell membranes during Neurotransmission
Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/09/10/synaptotagmin-functions-as-a-calcium-sensor-how-calcium-ions-regulate-the-fusion-of-vesicles-with-cell-membranes-during-neurotransmission/

The material presented is very focused, and cannot be found elsewhere in Pharmaceutical Intelligence with respedt to genetics and heart disease.  However, there are other articles that may be of interest to the reader.

Volume Three: Etiologies of Cardiovascular Diseases – Epigenetics, Genetics & Genomics

Curators: Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/biomed-e-books/series-a-e-books-on-cardiovascular-diseases/volume-three-etiologies-of-cardiovascular-diseases-epigenetics-genetics-genomics/

PART 3.  Determinants of Cardiovascular Diseases: Genetics, Heredity and Genomics Discoveries

3.2 Leading DIAGNOSES of Cardiovascular Diseases covered in Circulation: Cardiovascular Genetics, 3/2010 – 3/2013

The Diagnoses covered include the following – relevant to this discussion

  • MicroRNA in Serum as Bimarker for Cardiovascular Pathologies: acute myocardial infarction, viral myocarditis, diastolic dysfunction, and acute heart failure
  • Genomics of Ventricular arrhythmias, A-Fib, Right Ventricular Dysplasia, Cardiomyopathy
  • Heredity of Cardiovascular Disorders Inheritance

3.2.1: Heredity of Cardiovascular Disorders Inheritance

The implications of heredity extend beyond serving as a platform for genetic analysis, influencing diagnosis,

  1. prognostication, and
  2. treatment of both index cases and relatives, and
  3. enabling rational targeting of genotyping resources.

This review covers acquisition of a family history, evaluation of heritability and inheritance patterns, and the impact of inheritance on subsequent components of the clinical pathway.

SOURCE:   Circulation: Cardiovascular Genetics.2011; 4: 701-709.  http://dx.doi.org/10.1161/CIRCGENETICS.110.959379

3.2.2: Myocardial Damage

3.2.2.1 MicroRNA in Serum as Biomarker for Cardiovascular Pathologies: acute myocardial infarction, viral myocarditis,  diastolic dysfunction, and acute heart failure

Increased MicroRNA-1 and MicroRNA-133a Levels in Serum of Patients With Cardiovascular Disease Indicate Myocardial Damage
Y Kuwabara, Koh Ono, T Horie, H Nishi, K Nagao, et al.
SOURCE:  Circulation: Cardiovascular Genetics. 2011; 4: 446-454   http://dx.doi.org/10.1161/CIRCGENETICS.110.958975

3.2.2.2 Circulating MicroRNA-208b and MicroRNA-499 Reflect Myocardial Damage in Cardiovascular Disease

MF Corsten, R Dennert, S Jochems, T Kuznetsova, Y Devaux, et al.
SOURCE: Circulation: Cardiovascular Genetics. 2010; 3: 499-506.  http://dx.doi.org/10.1161/CIRCGENETICS.110.957415

3.2.4.2 Large-Scale Candidate Gene Analysis in Whites and African Americans Identifies IL6R Polymorphism in Relation to Atrial Fibrillation

The National Heart, Lung, and Blood Institute’s Candidate Gene Association Resource (CARe) Project
RB Schnabel, KF Kerr, SA Lubitz, EL Alkylbekova, et al.
SOURCE:  Circulation: Cardiovascular Genetics.2011; 4: 557-564   http://dx.doi.org/10.1161/CIRCGENETICS.110.959197

 Weighted Gene Coexpression Network Analysis of Human Left Atrial Tissue Identifies Gene Modules Associated With Atrial Fibrillation

N Tan, MK Chung, JD Smith, J Hsu, D Serre, DW Newton, L Castel, E Soltesz, G Pettersson, AM Gillinov, DR Van Wagoner and J Barnard
From the Cleveland Clinic Lerner College of Medicine (N.T.), Department of Cardiovascular Medicine (M.K.C., D.W.N.), and Department of Thoracic & Cardiovascular Surgery (E.S., G.P., A.M.G.); and Department of Cellular & Molecular Medicine (J.D.S., J.H.), Genomic Medicine Institute (D.S.), Department of Molecular Cardiology (L.C.), and Department of Quantitative Health Sciences (J.B.), Cleveland Clinic Lerner Research Institute, Cleveland, OH
Circ Cardiovasc Genet. 2013;6:362-371; http://dx.doi.org/10.1161/CIRCGENETICS.113.000133
http://circgenetics.ahajournals.org/content/6/4/362   The online-only Data Supplement is available at http://circgenetics.ahajournals.org/lookup/suppl/doi:10.1161/CIRCGENETICS.113.000133/-/DC1

Background—Genetic mechanisms of atrial fibrillation (AF) remain incompletely understood. Previous differential expression studies in AF were limited by small sample size and provided limited understanding of global gene networks, prompting the need for larger-scale, network-based analyses.

Methods and Results—Left atrial tissues from Cleveland Clinic patients who underwent cardiac surgery were assayed using Illumina Human HT-12 mRNA microarrays. The data set included 3 groups based on cardiovascular comorbidities: mitral valve (MV) disease without coronary artery disease (n=64), coronary artery disease without MV disease (n=57), and lone AF (n=35). Weighted gene coexpression network analysis was performed in the MV group to detect modules of correlated genes. Module preservation was assessed in the other 2 groups. Module eigengenes were regressed on AF severity or atrial rhythm at surgery. Modules whose eigengenes correlated with either AF phenotype were analyzed for gene content. A total of 14 modules were detected in the MV group; all were preserved in the other 2 groups. One module (124 genes) was associated with AF severity and atrial rhythm across all groups. Its top hub gene, RCAN1, is implicated in calcineurin-dependent signaling and cardiac hypertrophy. Another module (679 genes) was associated with atrial rhythm in the MV and coronary artery disease groups. It was enriched with cell signaling genes and contained cardiovascular developmental genes including TBX5.

Conclusions—Our network-based approach found 2 modules strongly associated with AF. Further analysis of these modules may yield insight into AF pathogenesis by providing novel targets for functional studies. (Circ Cardiovasc Genet. 2013;6:362-371.)

Key Words: arrhythmias, cardiac • atrial fibrillation • bioinformatics • gene coexpression • gene regulatory networks • genetics • microarrays

Introduction

trial fibrillation (AF) is the most common sustained car­diac arrhythmia, with a prevalence of ≈1% to 2% in the general population.1,2 Although AF may be an isolated con­dition (lone AF [LAF]), it often occurs concomitantly with other cardiovascular diseases, such as coronary artery disease (CAD) and valvular heart disease.1 In addition, stroke risk is increased 5-fold among patients with AF, and ischemic strokes attributed to AF are more likely to be fatal.1 Current antiarrhythmic drug therapies are limited in terms of efficacy and safety.1,3,4 Thus, there is a need to develop better risk pre­diction tools as well as mechanistically targeted therapies for AF. Such developments can only come about through a clearer understanding of its pathogenesis.

Family history is an established risk factor for AF. A Danish Twin Registry study estimated AF heritability at 62%, indicating a significant genetic component.5 Substantial progress has been made to elucidate this genetic basis. For example, genome-wide association studies (GWASs) have identified several susceptibil­ity loci and candidate genes linked with AF. Initial studies per­formed in European populations found 3 AF-associated genomic loci.6–9 Of these, the most significant single-nucleotide polymor-phisms (SNPs) mapped to an intergenic region of chromosome 4q25. The closest gene in this region, PITX2, is crucial in left-right asymmetrical development of the heart and thus seems promising as a major player in initiating AF.10,11 A large-scale GWAS meta-analysis discovered 6 additional susceptibility loci, implicating genes involved in cardiopulmonary development, ion transport, and cellular structural integrity.12

Differential expression studies have also provided insight into the pathogenesis of AF. A study by Barth et al13 found that about two-thirds of the genes expressed in the right atrial appendage were downregulated during permanent AF, and that many of these genes were involved in calcium-dependent signaling pathways. In addition, ventricular-predominant genes were upregulated in right atrial appendages of sub­jects with AF.13 Another study showed that inflammatory and transcription-related gene expression was increased in right atrial appendages of subjects with AF versus controls.14 These results highlight the adaptive responses to AF-induced stress and ischemia taking place within the atria.

Despite these advances, much remains to be discovered about the genetic mechanisms of AF. The AF-associated SNPs found thus far only explain a fraction of its heritability15; furthermore, the means by which the putative candidate genes cause AF have not been fully established.9,15,16 Additionally, previous dif­ferential expression studies in human tissue were limited to the right atrial appendage, had small sample sizes, and provided little understanding of global gene interactions.13,14 Weighted gene coexpression network analysis (WGCNA) is a technique to construct gene modules within a network based on correla­tions in gene expression (ie, coexpression).17,18 WGCNA has been used to study genetically complex diseases, such as meta­bolic syndrome,19 schizophrenia,20 and heart failure.21 Here, we obtained mRNA expression profiles from human left atrial appendage tissue and implemented WGCNA to identify gene modules associated with AF phenotypes.

Methods

Subject Recruitment

From 2001 to 2008, patients undergoing cardiac surgery at the Cleveland Clinic were prospectively screened and recruited. Informed consent for research use of discarded atrial tissues was ob­tained from each patient by a study coordinator during the presur­gical visit. Demographic and clinical data were obtained from the Cardiovascular Surgery Information Registry and by chart review. Use of human atrial tissues was approved by the Institutional Review Board of the Cleveland Clinic.

Table S1: Clinical definitions of cardiovascular phenotype groups

Criterion Type Mitral Valve (MV) Disease Coronary Artery Disease (CAD) Lone Atrial Fibrillation (LAF)
Inclusion Criteria Surgical indication – Surgical indication – History of atrial fibrillation
mitral valve repair or replacement coronary artery bypass graft
Surgical indication
– MAZE procedure
Preserved ejection fraction (≥50%)
Exclusion Criteria Significant coronary artery disease: Significant mitral valve disease: Significant
coronary artery
– Significant (≥50%) stenosis – Documented echocardiography disease:
 in at least finding of – Significant
one coronary artery  mitral regurgitation (≥3) or (≥50%) stenosis in
via cardiac catheterization mitral stenosis at least one
– History of revascularization – History of mitral valve coronary artery via
(percutaneous coronary intervention or coronary artery bypass graft surgery)  repair or replacement cardiac catheterization
– History of revascularization
(percutaneous coronary intervention or coronary artery bypass graft surgery)
Significant valvular heart disease:
-Documented echocardiography finding of valvular regurgitation (≥3) or stenosis
-History of valve repair or replacement

RNA Microarray Isolation and Profiling

Left atria appendage specimens were dissected during cardiac surgery and stored frozen at −80°C. Total RNA was extracted using the Trizol technique. RNA samples were processed by the Cleveland Clinic Genomics Core. For each sample, 250-ng RNA was reverse tran­scribed into cRNA and biotin-UTP labeled using the TotalPrep RNA Amplification Kit (Ambion, Austin, TX). cRNA was quantified using a Nanodrop spectrophotometer, and cRNA size distribution was as­sessed on a 1% agarose gel. cRNA was hybridized to Illumina Human HT-12 Expression BeadChip arrays (v.3). Arrays were scanned using a BeadArray reader.

Expression Data Preprocessing

Raw expression data were extracted using the beadarray package in R, and bead-level data were averaged after log base-2 transformation. Background correction was performed by fitting a normal-gamma deconvolution model using the NormalGamma R package.22 Quantile normalization and batch effect adjustment with the ComBat method were performed using R.23 Probes that were not detected (at a P<0.05 threshold) in all samples as well as probes with relatively lower vari­ances (interquartile range ≤log2[1.2]) were excluded.

The WGCNA approach requires that genes be represented as sin­gular nodes in such a network. However, a small proportion of the genes in our data have multiple probe mappings. To facilitate the representation of singular genes within the network, a probe must be selected to represent its associated gene. Hence, for genes that mapped to multiple probes, the probe with the highest mean expres­sion level was selected for analysis (which often selects the splice isoform with the highest expression and signal-to-noise ratio), result­ing in a total of 6168 genes.

Defining Training and Test Sets

Currently, no large external mRNA microarray data from human left atrial tissues are publicly available. To facilitate internal validation of results, we divided our data set into 3 groups based on cardiovascular comorbidities: mitral valve (MV) disease without CAD (MV group; n=64), CAD without MV disease (CAD group; n=57), and LAF (LAF group; n=35). LAF was defined as the presence of AF without concomitant structural heart disease, according to the guidelines set by the European Society of Cardiology.1 The MV group, which was the largest and had the most power for detecting significant modules, served as the training set for module derivation, whereas the other 2 groups were designated test sets for module reproducibility. To mini­mize the effect of population stratification, the data set was limited to white subjects. Differences in clinical characteristics among the groups were assessed using Kruskal–Wallis rank-sum tests for con­tinuous variables and Pearson x2 test for categorical variables.

Weight Gene Coexpression Network Analysis

WGCNA is a systems-biology method to identify and characterize gene modules whose members share strong coexpression. We applied previously validated methodology in this analysis.17 Briefly, pair-wise gene (Pearson) correlations were calculated using the MV group data set. A weighted adjacency matrix was then constructed. I is a soft-thresholding pa­rameter that provides emphasis on stronger correlations over weaker and less meaningful ones while preserving the continuous nature of gene–gene relationships. I=3 was selected in this analysis based on the criterion outlined by Zhang and Horvath17 (see the online-only Data Supplement).

Next, the topological overlap–based dissimilarity matrix was com­puted from the weighted adjacency matrix. The topological overlap, developed by Ravasz et al,24 reflects the relative interconnectedness (ie, shared neighbors) between 2 genes.17 Hence, construction of the net­work dendrogram based on this dissimilarity measure allows for the identification of gene modules whose members share strong intercon-nectivity patterns. The WGCNA cutreeDynamic R function was used to identify a suitable cut height for module identification via an adap­tive cut height selection approach.18 Gene modules, defined as branches of the network dendrogram, were assigned colors for visualization.

Network Preservation Analysis

Module preservation between the MV and CAD groups as well as the MV and LAF groups was assessed using network preservation statis­tics as described in Langfelder et al.25 Module density–based statistics (to assess whether genes in each module remain highly connected in the test set) and connectivity-based statistics (to assess whether con­nectivity patterns between genes in the test set remain similar com­pared with the training set) were considered in this analysis.25 In each comparison, a Z statistic representing a weighted summary of module density and connectivity measures was computed for every module (Zsummary). The Zsummary score was used to evaluate module preserva­tion, with values ≥8 indicating strong preservation, as proposed by Langfelder et al.25 The WGCNA R function network preservation was used to implement this analysis.25

Table S2: Network preservation analysis between the MV and CAD groups – size and Zsummary scores of gene modules detected.

Module Module Size

ZSummary

Black 275 15.52
Blue 964 44.79
Brown 817 12.80
Cyan 119 13.42
Green 349 14.27
Green-Yellow 215 19.31
Magenta 239 15.38
Midnight-Blue 83 15.92
Pink 252 23.31
Purple 224 16.96
Red 278 17.30
Salmon 124 13.84
Tan 679 28.48
Turquoise 1512 44.03


Table S3: Network preservation analysis between the MV and LAF groups – size and Zsummary scores of gene modules detected

Module Module Size ZSummary
Black 275 13.14
Blue 964 39.26
Brown 817 14.98
Cyan 119 11.46
Green 349 14.91
Green-Yellow 215 20.99
Magenta 239 18.58
Midnight-Blue 83 13.87
Pink 252 19.10
Purple 224 8.80
Red 278 16.62
Salmon 124 11.57
Tan 679 28.61
Turquoise 1512 42.07

Clinical Significance of Preserved Modules

Principal component analysis of the expression data for each gene module was performed. The first principal component of each mod­ule, designated the eigengene, was identified for the 3 cardiovascular disease groups; this served as a summary expression measure that explained the largest proportion of the variance of the module.26 Multivariate linear regression was performed with the module ei-gengenes as the outcome variables and AF severity (no AF, parox­ysmal AF, persistent AF, permanent AF) as the predictor of interest (adjusting for age and sex). A similar regression analysis was per­formed with atrial rhythm at surgery (no AF history, AF history in sinus rhythm, AF history in AF rhythm) as the predictor of interest. The false discovery rate method was used to adjust for multiple com­parisons. Modules whose eigengenes associated with AF severity and atrial rhythm were identified for further analysis.

In addition, hierarchical clustering of module eigengenes and se­lected clinical traits (age, sex, hypertension, cholesterol, left atrial size, AF state, and atrial rhythm) was used to identify additional module–trait associations. Clusters of eigengenes/traits were detected based on a dissimilarity measure D, as given by

D=1−cor(Vi,Vj),i≠j                                                                              (3)

where V=the eigengene or clinical trait.

Enrichment Analysis

Gene modules significantly associated with AF severity and atrial rhythm were submitted to Ingenuity Pathway Analysis (IPA) to determine enrichment for functional/disease categories. IPA is an application of gene set over-representation analysis; for each dis-ease/functional category annotation, a P value is calculated (using Fisher exact test) by comparing the number of genes from the mod­ule of interest that participate in the said category against the total number of participating genes in the background set.27 All 6168 genes in the current data set served as the background set for the enrichment analysis.

Hub Gene Analysis

Hub genes are defined as genes that have high intramodular connectivity17,20

Alternatively, they may also be defined as genes with high module membership21,25

Both definitions were used to identify the hub genes of modules associated with AF phenotype.

To confirm that the hub genes identified were themselves associ­ated with AF phenotype, the expression data of the top 10 hub genes (by intramodular connectivity) were regressed on atrial rhythm (ad­justing for age and sex). In addition, eigengenes of AF-associated modules were regressed on their respective (top 10) hub gene expres­sion profiles, and the model R2 indices were computed.

Membership of AF-Associated Candidate Genes From Previous Studies

Previous GWAS studies identified multiple AF-associated SNPs.8,9,12,15,28 We selected candidate genes closest to or containing these SNPs and identified their module locations as well as their clos­est within-module partners (absolute Pearson correlations).

Sensitivity Analysis of Soft-Thresholding Parameter

To verify that the key results obtained from the above analysis were robust with respect to the chosen soft-thresholding parameter (I=3), we repeated the module identification process using I=5. The eigen-genes of the detected modules were computed and regressed on atrial rhythm (adjusting for age and sex). Modules significantly associated with atrial rhythm in ≥2 groups of data set were compared with the AF phenotype–associated modules from the original analysis.

Results

Subject Characteristics

Table 1 describes the clinical characteristics of the cardiac surgery patients who were recruited for the study. Subjects in the LAF group were generally younger and less likely to be a current smoker (P=2.0×10−4 and 0.032, respectively). Subjects in the MV group had lower body mass indices (P=2.7×10−6), and a larger proportion had paroxysmal AF compared with the other 2 groups (P=0.033).

Table 1. Clinical Characteristics of Study Subjects

Characteristics

MV Group (n=64)

CAD Group (n=57)

LAF Group (n=35)

P Value*

Age, median y (1st–3rd quartiles)

60 (51.75–67.25)

64 (58.00–70.00)

56 (45.50–60.50)

2.0×10−4

Sex, female (%) 19 (29.7) 6 (10.5)

7 (20.0)

0.033

BMI, median (1st–3rd quartiles)

25.97 (24.27–28.66)

29.01 (27.06–32.11)

29.71 (26.72–35.10)

2.7×10−6

Current smoker (%) 29 (45.3) 35 (61.4)

12 (21.1)

0.032

Hypertension (%) 21 (32.8) 39 (68.4)

16 (45.7)

4.4×10−4

AF severity (%)
No AF 7 (10.9) 7 (12.3)

0 (0.0)

0.033

Paroxysmal 19 (29.7) 10 (17.5)

7 (20.0)

Persistent 30 (46.9) 26 (45.6)

15 (42.9)

Permanent 8 (12.5) 14 (24.6)

13 (37.1)

Atrial rhythm at surgery (%)
No AF history in sinus rhythm 7 (10.9) 7 (12.3)

0 (0)

0.065

AF history in sinus rhythm 28 (43.8) 16 (28.1)

11 (31.4)

AF History in AF rhythm 29 (45.3) 34 (59.6)

24 (68.6)

Gene Coexpression Network Construction and Module Identificationsee document at  http://circgenetics.ahajournals.org/content/6/4/362

A total of 14 modules were detected using the MV group data set (Figure 1), with module sizes ranging from 83 genes to 1512 genes; 38 genes did not share similar coexpression with the other genes in the network and were therefore not included in any of the identified modules

Figure 1. Network dendrogram (top) and colors of identified modules (bottom).

Figure 1. Network dendrogram (top) and colors of identified modules (bottom). The dendrogram was constructed using the topological overlap matrix as the similarity measure. Modules corresponded to branches of the dendrogram and were assigned colors for visualization.

Network Preservation Analysis Revealed Strong Preservation of All Modules Between the Training and Test Sets

All 14 modules showed strong preservation across the CAD and LAF groups in both comparisons, with Z [summary]  scores of >10 in most modules (Figure 2). No major deviations in the Z [summary] score distributions for the 2 comparisons were noted, indicating that modules were preserved to a similar extent across the 2 groups

Figure 2. Preservation of mod-ules between mitral valve (MV) and coronary artery disease

Figure 2. Preservation of mod­ules between mitral valve (MV) and coronary artery disease (CAD) groups (left), and MV and lone atrial fibrillation (LAF) groups (right). A Zsummary sta­tistic was computed for each module as an overall measure of its preservation relating to density and connectivity. All modules showed strong pres­ervation in both comparisons with Zsummary scores >8 (red dot­ted line).

Regression Analysis of Module Eigengene Profiles Identified 2 Modules Associated With AF Severity and Atrial Rhythm

Table IV in the online-only Data Supplement summarizes the proportion of variance explained by the first 3 principal components for each module. On average, the first principal component (ie, the eigengene) explained ≈18% of the total variance of its associated module. For each group, the mod­ule eigengenes were extracted and regressed on AF severity (with age and sex as covariates). The salmon module (124 genes) eigengene was strongly associated with AF severity in the MV and CAD groups (P=1.7×10−6 and 5.2×10−4, respec­tively); this association was less significant in the LAF group (P=9.0×10−2). Eigengene levels increased with worsening AF severity across all 3 groups, with the greatest stepwise change taking place between the paroxysmal AF and per­sistent AF categories (Figure 3A). When the module eigen-genes were regressed on atrial rhythm, the salmon module eigengene showed significant association in all groups (MV: P=1.1×10−14; CAD: P=1.36×10−6; LAF: P=2.1×10−4). Eigen-gene levels were higher in the AF history in AF rhythm cat­egory (Figure 3B).

Table S4: Proportion of variance explained by the principal components for each module.

Dataset
Group

Principal
Component

Black

Blue

Brown

Cyan

Green

Green-
Yellow

Magenta

Mitral

1

20.5% 22.2% 20.1% 21.8% 21.4% 22.8% 19.6%

2

4.1% 3.6% 4.8% 5.7% 4.5% 5.9% 3.9%

3

3.4% 3.1% 3.8% 4.4% 3.9% 3.7% 3.7%

CAD

1

12.5% 18.6% 7.1% 16.8% 12.2% 20.3% 12.8%

2

6.0% 5.5% 5.0% 7.0% 5.5% 6.1% 6.4%

3

4.9% 4.1% 4.4% 6.5% 4.8% 4.4% 4.8%

LAF

1

14.0% 16.6% 11.7% 14.3% 14.7% 20.8% 20.2%

2

8.9% 8.5% 7.6% 9.3% 7.3% 11.1% 6.9%

3

6.5% 6.3% 5.5% 8.2% 6.1% 5.3% 6.2%

Dataset
Group

Principal
Component

Midnight- Blue

Pink

Purple

Red

Salmon

Tan

Turquoise

Mitral

1

28.5% 22.6% 18.7% 20.5% 22.3% 19.0% 25.8%

2

4.6% 6.0% 4.7% 4.1% 6.9% 4.0% 3.5%

3

4.2% 4.2% 4.2% 3.5% 4.0% 3.6% 3.3%

CAD

1

23.4% 17.1% 15.5% 15.0% 18.0% 14.6% 18.2%

2

7.4% 8.6% 6.0% 6.4% 7.2% 5.8% 6.6%

3

5.1% 5.4% 5.3% 5.4% 6.2% 5.1% 4.5%

LAF

1

23.5% 18.4% 12.0% 15.9% 16.9% 13.7% 16.5%

2

7.9% 8.5% 9.8% 9.4% 9.5% 9.1% 9.6%

3

6.7% 7.0% 6.6% 6.0% 6.9% 6.8% 6.3%

Figure 3. Boxplots of salmon module eigengene expression levels with respect to atrial fibrillation (AF) severity (A) and atrial rhythm (B).

Figure 3. Boxplots of salmon module eigengene expression levels with respect to atrial fibrillation (AF) severity (A) and atrial rhythm (B).
A, Eigengene expression correlated positively with AF severity, with the largest stepwise increase between the paroxysmal AF and per­manent AF categories. B, Eigengene expression was highest in the AF history in AF rhythm category in all 3 groups. CAD indicates coro­nary artery disease; LAF, lone AF; and MV, mitral valve.

The regression analysis also revealed statistically significant associations between the tan module (679 genes) eigengene and atrial rhythm in the MV and CAD groups (P=5.8×10−4 and 3.4×10−2, respectively). Eigengene levels were lower in the AF history in AF rhythm category compared with the AF history in sinus rhythm category (Figure 4); this trend was also observed in the LAF group, albeit with weaker statistical evidence (P=0.15).

Figure 4. Boxplots of tan module eigengene expression levels with respect to atrial rhythm.

Figure 4. Boxplots of tan module eigengene expression levels with respect to atrial rhythm.
Eigengene expression levels were lower in the atrial fibrillation (AF) history in AF rhythm category compared with the AF history in sinus rhythm category. CAD indicates coronary artery disease; LAF, lone AF; and MV, mitral valve

Hierarchical Clustering of Eigengene Profiles With Clinical Traits

Hierarchical clustering was performed to identify relation­ships between gene modules and selected clinical traits. The salmon module clustered with AF severity and atrial rhythm; in addition, left atrial size was found in the same cluster, sug­gesting a possible relationship between salmon module gene expression and atrial remodeling (Figure 5A). Although the tan module was in a separate cluster from the salmon module, it was negatively correlated with both atrial rhythm and AF severity (Figure 5B).

Figure 5. Dendrogram (A) and correlation heatmap (B) of module eigengenes and clinical traits.

Figure 5. Dendrogram (A) and correlation heatmap (B) of module eigengenes and clinical traits

A, The salmon module eigengene but not the tan module eigengene clustered with atrial fibrillation (AF) severity, atrial rhythm, and left atrial size. B, AF severity and atrial rhythm at surgery correlated positively with the salmon module eigengene and negatively with the tan module eigengene. Arhythm indicates atrial rhythm at surgery; Chol, cholesterol; HTN, hypertension; and LASize, left atrial size.

IPA Enrichment Analysis of Salmon and Tan Modules

The salmon module was enriched in genes involved in cardio­vascular function and development (smallest P=4.4×10−4) and organ morphology (smallest P=4.4×10−4). In addition, the top disease categories identified included endocrine system disor­ders (smallest P=4.4×10−4) and cardiovascular disease (small­est P=2.59×10−3).

The tan module was enriched in genes involved in cell-to-cell signaling and interaction (smallest P=8.9×10−4) and cell death and survival (smallest P=1.5×10−3). Enriched disease categories included cancer (smallest P=2.2×10−4) and cardio­vascular disease (smallest P=4.5×10−4).

see document at  http://circgenetics.ahajournals.org/content/6/4/362

Hub Gene Analysis of Salmon and Tan Modules

We identified hub genes in the 2 modules based on intramod-ular connectivity and module membership. For the salmon module, the gene RCAN1 exhibited the highest intramodular connectivity and module membership. The top 10 hub genes (by intramodular connectivity) were significantly associated with atrial rhythm, with false discovery rate–adjusted P values ranging from 1.5×10−5 to 4.2×10−12. These hub genes accounted for 95% of the variation in the salmon module eigengene.

In the tan module, the top hub gene was CPEB3. The top 10 hub genes (by intramodular connectivity) correlated with atrial rhythm as well, although the statistical associations in the lower-ranked hub genes were relatively weaker (false discovery rate–adjusted P values ranging from 1.1×10−1 to 3.4×10−4). These hub genes explained 94% of the total varia­tion in the tan module eigengene.

The names and connectivity measures of the hub genes found in both modules are presented in Table 2.

Table 2. Top 10 Hub Genes in the Salmon (Left) and Tan (Right) Modules as Defined by Intramodular Connectivity and Module Membership

Salmon Module

Tan Module

Gene

IMC

Gene

MM

Gene

IMC

Gene

MM

RCAN1 8.2

RCAN1

0.81

CPEB3

43.3

CPEB3

0.85
DNAJA4 7.7

DNAJA4

0.81

CPLX3

42.4

CPLX3

0.84
PDE8B 7.7

PDE8B

0.80

NEDD4L

40.8

NEDD4L

0.83
PRKAR1A 6.9

PRKAR1A

0.77

SGSM1

40.7

SGSM1

0.82
PTPN4 6.7

PTPN4

0.75

UCKL1

39.0

UCKL1

0.81
SORBS2 6.0

FHL2

0.69

SOSTDC1

37.2

SOSTDC1

0.79
ADCY6 5.7

ADCY6

0.69

PRDX1

35.5

RCOR2

0.78
FHL2 5.7

SORBS2

0.68

RCOR2

35.4

EEF2K

0.77
BVES 5.4

DHRS9

0.67

NPPB

35.3

PRDX1

0.76
TMEM173 5.3

LAPTM4B

0.65

LRRN3

34.6

MMP11

0.76

A visualiza­tion of the salmon module is shown using the Cytoscape tool (Figure 6). A full list of the genes in the salmon and tan mod­ules is provided in the online-only Data Supplement.

Figure 6. Cytoscape visualization of genes in the salmon module.
Nodes representing genes with high intramodu-lar connectivities, such as RCAN1 and DNAJA4, appear larger in the network. Strong connections are visualized with darker lines, whereas weak connections appear more translucent

Figure 6. Cytoscape visualization of genes in the salmon module.

Membership of AF-Associated Candidate Genes From Previous Studies

The tan module contained MYOZ1, which was identified as a candidate gene from the recent AF meta-analysis. PITX2 was located in the green module (n=349), and ZFHX3 was located in the turquoise module (n=1512). The locations of other can­didate genes (and their closest partners) are reported in the online-only Data Supplement.

Sensitivity Analysis of Key Results

We repeated the WGCNA module identification approach using a different soft-thresholding parameter (β=5). One mod­ule (n=121) was found to be strongly associated with atrial rhythm at surgery across all 3 groups of data set, whereas another module (n=244) was associated with atrial rhythm at surgery in the MV and CAD groups. The first module over­lapped significantly with the salmon module in terms of gene membership, whereas most of the second modules’ genes were contained within the tan module. The top hub genes found in the salmon and tan modules remained present and highly connected in the 2 new modules identified with the dif­ferent soft-thresholding parameter.

Discussion

To our knowledge, our study is the first implementation of an unbiased, network-based analysis in a large sample of human left atrial appendage gene expression profiles. We found 2 modules associated with AF severity and atrial rhythm in 2 to 3 of our cardiovascular comorbidity groups. Functional analy­ses revealed significant enrichment of cardiovascular-related categories for both modules. In addition, several of the hub genes identified are implicated in cardiovascular disease and may play a role in AF initiation and progression.

In our study, WGCNA was used to construct modules based on gene coexpression, thereby reducing the net-work’s dimensionality to a smaller set of elements.17,21 Relating modulewise changes to phenotypic traits allowed statistically significant associations to be detected at a lower false discovery rate compared with traditional differential expression studies. Furthermore, shared functions and path­ways among genes in the modules could be inferred via enrichment analyses.

We divided our data set into 3 groups to verify the repro­ducibility of the modules identified by WGCNA; 14 modules were identified in the MV group in our gene network. All were strongly preserved in the CAD and LAF groups, suggesting that gene coexpression patterns are robust and reproducible despite differences in cardiovascular comorbidities.

The use of module eigengene profiles as representative summary measures has been validated in a number of studies.20,26 Additionally, we found that the eigengenes accounted for a significant proportion (average 18%) of gene expression variability in their respective modules. Regression analysis of the module eigengenes found 2 modules associated with AF severity and atrial rhythm in ≥2 groups of data set. The association between the salmon module eigengene and AF severity was statistically weaker in the LAF group (adjusted P=9.0×10−2). This was probably because of its significantly smaller sample size compared with the MV and CAD groups. Despite this weaker association, the relationship between the salmon module eigengene and AF severity remained consistent among the 3 groups (Figure 3A). Similarly, the lack of statistical significance for the association between the tan module eigengene and atrial rhythm at surgery in the LAF group was likely driven by the smaller sample size and (by definition) lack of samples in the no AF category.

A major part of our analysis focused on the identifica­tion of module hub genes. Hubs are connected with a large number of nodes; disruption of hubs therefore leads to wide­spread changes within the network. This concept has powerful applications in the study of biology, genetics, and disease.29,30 Although mutations of peripheral genes can certainly lead to disease, gene network changes are more likely to be motivated by changes in hub genes, making them more biologically inter­esting targets for further study.17,29,31 Indeed,

  • the hub genes of the salmon and tan modules accounted for the vast majority of the variation in their respective module eigengenes, signaling their importance in driving gene module behavior.

The hub genes identified in the salmon and tan modules were significantly associated with AF phenotype overall. It was noted that this association was statistically weaker for the lower-ranked hub genes in the tan module. This highlights an important aspect and strength of WGCNA—to be able to capture module-wide changes with respect to disease despite potentially weaker associations among individual genes.

The implementation of WGCNA necessitated the selection of a soft-thresholding parameter 13. Unlike hard-thresholding (where gene correlations below a certain value are shrunk to zero), the soft-thresholding approach gives greater weight to stronger correlations while maintaining the continuous nature of gene–gene relationships. We selected a 13 value of 3 based on the criteria outlined by Zhang and Horvath.17 His team and other investigators have demonstrated that module identifica­tion is robust with respect to the 13 parameter.17,19–21 In our data, we were also able to reproduce the key findings reported with a different, larger 13 value, thereby verifying the stability of our results relating to 13.

The salmon module (124 genes) was associated with both AF phenotypes; furthermore, IPA analysis of its gene con­tents suggested enrichment in cardiovascular development as well as disease. Its eigengene increased with worsening AF severity, with the largest stepwise change occurring between the paroxysmal AF and persistent AF categories (Figure 3). Hence,

  • the gene expression changes within the salmon mod­ule may reflect the later stages of AF pathophysiology.

The top hub gene of the salmon module was RCAN1 (reg­ulator of calcineurin 1). Calcineurin is a cytoplasmic Ca2+/ calmodulin-dependent protein phosphatase that stimulates cardiac hypertrophy via its interactions with NFAT and L-type Ca2+ channels.32,33 RCAN1 is known to inhibit calcineurin and its associated pathways.32,34 However, some data suggest that RCAN1 may instead function as a calcineurin activator when highly expressed and consequently potentiate hypertrophic signaling.35 Thus,

  • perturbations in RCAN1 levels (attribut­able to genetic variants or mutations) may cause an aberrant switching in function, which in turn triggers atrial remodeling and arrhythmogenesis.

Other hub genes found in the salmon module are also involved in cardiovascular development and function and may be potential targets for further study.

  • DNAJA4 (DnaJ homolog, subfamily A, member 4) regulates the trafficking and matu­ration of KCNH2 potassium channels, which have a promi­nent role in cardiac repolarization and are implicated in the long-QT syndromes.36

FHL2 (four-and-a-half LIM domain protein 2) interacts with numerous cellular components, including

  1. actin cytoskeleton,
  2. transcription machinery, and
  3. ion channels.37

FHL2 was shown to enhance the hypertrophic effects of isoproterenol, indicating that

  • FHL2 may modulate the effect of environmental stress on cardiomyocyte growth.38
  • FHL2 also interacts with several potassium channels in the heart, such as KCNQ1, KCNE1, and KCNA5.37,39

Additionally, blood vessel epicardial substance (BVES) and other members of its family were shown to be highly expressed in cardiac pacemaker cells. BVES knockout mice exhibited sinus nodal dysfunction, suggesting that BVES regulates the development of the cardiac pacemaking and conduction system40 and may therefore be involved in the early phase of AF development.

The tan module (679 genes) eigengene was negatively correlated with atrial rhythm in the MV and CAD groups (Figure 4); this may indicate a general decrease in gene expres­sion of its members in fibrillating atrial tissue. IPA analysis revealed enrichment in genes involved in cell signaling as well as apoptosis. The top-ranked hub gene, cytoplasmic polyade-nylation element binding protein 3 (CPEB3), regulates mRNA translation and has been associated with synaptic plasticity and memory formation.41 The role of CPEB3 in the heart is currently unknown, so further exploration via animal model studies may be warranted.

Natriuretic peptide-precursor B (NPPB), another highly interconnected hub gene, produces a precursor peptide of brain natriuretic peptide, which

  • regulates blood pressure through natriuresis and vasodilation.42

(NPPB) gene variants have been linked with diabetes mellitus, although associations with cardiac phenotypes are less clear.42 TBX5 and GATA4, which play important roles in the embryonic heart development,43 were members of the tan module. Although not hub genes, they may also contribute toward developmental sus­ceptibility of AF. In addition, TBX5 was previously reported to be near an SNP associated with PR interval and AF in separate large-scale GWAS studies.12,28 MYOZ1, another candidate gene identified in the recent AF GWAS meta-analysis, was found to be a member as well; it associates with proteins found in the Z-disc of skeletal and cardiac muscle and may suppress calcineurin-dependent hypertrophic signaling.12

Some, but not all, of the candidate genes found in previous GWAS studies were located in the AF-associated modules. One possible explanation for this could be the difference in sample sizes. The meta-analysis involved thousands of indi­viduals, whereas the current study had <100 in each group of data set, which limited the power to detect significant differ­ences between levels of AF phenotype even with the module-wise approach. Additionally, transcription factors like PITX2 are most highly expressed during the fetal phase of develop­ment. Perturbations in these genes (attributable to genetic variants or mutations) may therefore initiate the development of AF at this stage and play no significant role in adults (when we obtained their tissue samples).

Limitations in Study

We noted several limitations in this study. First, no human left atrial mRNA data set of adequate size currently exists publicly. Hence, we were unable to validate our results with an external, independent data set. However, the network pres­ervation assessment performed within our data set showed strong preservation in all modules, indicating that our findings are robust and reproducible.

Although the module eigengenes captured a significant pro­portion of module variance, a large fraction of variability did remain unaccounted for, which may limit their use as repre­sentative summary measures.

We extracted RNA from human left atrial appendage tis­sue, which consists primarily of cardiomyocytes and fibro­blasts. Atrial fibrosis is known to occur with AF-associated remodeling.44 As such, the cardiomyocyte to fibroblast ratio is likely to change with different levels of AF severity, which in turn influences the amount of RNA extracted from each cell type. Hence, true differences in gene expression (and coexpression) within cardiomyocytes may be confounded by changes in cellular composition attributable to atrial remod­eling. Also, there may be significant regional heterogeneity in the left atrium with respect to structure, cellular composi­tion, and gene expression,45 which may limit the generaliz-ability of our results to other parts of the left atrium.

All subjects in the study were whites to minimize the effects of population stratification. However, it is recognized that the genetic basis of AF may differ among ethnic groups.9 Thus, our results may not be generalizable to other ethnicities.

Finally, it is possible for genes to be involved in multiple processes and functions that require different sets of genes. However, WGCNA does not allow for overlapping modules to be formed. Thus,

  • this limits the method’s ability to character­ize such gene interactions.

Conclusions

In summary, we constructed a weighted gene coexpression network based on RNA expression data from the largest collection of human left atrial appendage tissue specimens to date. We identified 2 gene modules significantly associated with AF severity or atrial rhythm at surgery. Hub genes within these modules may be involved in the initiation or progression of AF and may therefore be candidates for functional stud­ies.

Refererences

1. European Heart Rhythm Association, European Association for Cardio-Thoracic Surgery, Camm AJ, Kirchhof P, Lip GY, Schotten U, et al. Guidelines for the management of atrial fibrillation: the task force for the management of atrial fibrillation of the European Society of Cardiology (ESC). Eur Heart J. 2010;31:2369–2429.

2. Lemmens R, Hermans S, Nuyens D, Thijs V. Genetics of atrial fibrilla­tion and possible implications for ischemic stroke. Stroke Res Treat. 2011;2011:208694.

3. Wann LS, Curtis AB, January CT, Ellenbogen KA, Lowe JE, Estes NA III, et al; ACCF/AHA/HRS. 2011 ACCF/AHA/HRS focused update on the management of patients with atrial fibrillation (Updating the 2006 Guideline): a report of the American College of Cardiology Foundation/ American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2011;57:223–242.

4. Dobrev D, Carlsson L, Nattel S. Novel molecular targets for atrial fibrilla­tion therapy. Nat Rev Drug Discov. 2012;11:275–291.

5. Christophersen IE, Ravn LS, Budtz-Joergensen E, Skytthe A, Haunsoe S, Svendsen JH, et al. Familial aggregation of atrial fibrillation: a study in Danish twins. Circ Arrhythm Electrophysiol. 2009;2:378–383.

6. Gudbjartsson DF, Arnar DO, Helgadottir A, Gretarsdottir S, Holm H, Sig-urdsson A, et al. Variants conferring risk of atrial fibrillation on chromo­some 4q25. Nature. 2007;448:353–357.

7. Ellinor PT, Lunetta KL, Glazer NL, Pfeufer A, Alonso A, Chung MK, et al. Common variants in KCNN3 are associated with lone atrial fibrillation. Nat Genet. 2010;42:240–244.

8. Benjamin EJ, Rice KM, Arking DE, Pfeufer A, van Noord C, Smith AV, et al. Variants in ZFHX3 are associated with atrial fibrillation in individuals of European ancestry. Nat Genet. 2009;41:879–881.

9. Sinner MF, Ellinor PT, Meitinger T, Benjamin EJ, Kääb S. Genome-wide association studies of atrial fibrillation: past, present, and future. Cardio-vasc Res. 2011;89:701–709.

10. Clauss S, Kääb S. Is Pitx2 growing up? Circ Cardiovasc Genet. 2011;4:105–107.

11. Kirchhof P, Kahr PC, Kaese S, Piccini I, Vokshi I, Scheld HH, et al. PITX2c is expressed in the adult left atrium, and reducing Pitx2c expres­sion promotes atrial fibrillation inducibility and complex changes in gene expression. Circ Cardiovasc Genet. 2011;4:123–133.

12. Ellinor PT, Lunetta KL, Albert CM, Glazer NL, Ritchie MD, Smith AV, et al. Meta-analysis identifies six new susceptibility loci for atrial fibrillation. Nat Genet. 2012;44:670–675.

13. Barth AS, Merk S, Arnoldi E, Zwermann L, Kloos P, Gebauer M, et al. Reprogramming of the human atrial transcriptome in permanent atrial fi­brillation: expression of a ventricular-like genomic signature. Circ Res. 2005;96:1022–1029.

Continues to 45.  see

http://circgenetics.ahajournals.org/content/6/4/362

CLINICAL PERSPECTIVE

Atrial fibrillation is the most common sustained cardiac arrhythmias in the United States. The genetic and molecular mecha­nisms governing its initiation and progression are complex, and our understanding of these mechanisms remains incomplete despite recent advances via genome-wide association studies, animal model experiments, and differential expression studies. In this study, we used weighted gene coexpression network analysis to identify gene modules significantly associated with atrial fibrillation in a large sample of human left atrial appendage tissues. We further identified highly interconnected genes (ie, hub genes) within these gene modules that may be novel candidates for functional studies. The discovery of the atrial fibrillation-associated gene modules and their corresponding hub genes provide novel insight into the gene network changes that occur with atrial fibrillation, and closer study of these findings can lead to more effective targeted therapies for disease management.

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Latest research efforts reported in the San Antonio Breast Cancer Symposium, 2012

Curator: Ritu Saxena, Ph.D.

‘Triple negative breast cancer’ or TNBC, as the name suggests, is a classification of breast cancers lacking the expression of estrogen receptor (ER) and progesterone receptor expression as well as amplification of the human epidermal growth factor receptor 2 (HER2).

Unlike other breast cancer types, treating TNBC is a challenge mainly because of the absence of well-defined molecular targets and because of disease heterogeneity. Currently, neoadjuvant chemotherapies are in use to treat TNBC patients. Some, around 30%, patients respond completely to neoadjuvant chemotherapy and have good outcomes after surgery. However, if there is a residual disease after therapy, outcomes are poor.

Therefore, current focus of the field is to first understand the complexity of the disease, both at genomic and molecular level and look for targets. Also, several combination chemotherapies are currently under trial to determine the efficacy, overall response rate, progression-free survival and other relevant factors for patients suffering with different forms of TNBC.

Recently, in the San Antonio Breast Cancer Symposium (SABCS 2012), several abstarcts related to TNBC research, both clinical and pre-clinical. Here is a compilation of some of the abstracts and their relevance in the field of TNBC research:

Triple Negative Breast Cancer: Subtypes, Molecular Targets, and Therapeutic Approaches, Pietenpol JA, Vanderbilt-Ingram Cancer Center; Vanderbilt University School of Medicine (Nashville, TN), Abstract no. ES2-2.

In order to better understand the complexity of TNBC, an integrative and comprehensive genomic and molecular analysis is required. The analysis would give important cues to developing and administering effective therapeutic agents. The group has compiled an extensive number of TNBC gene expression profiles and initiated molecular subtyping of the disease. Differential GE was used to designate 25 TNBC cell line models representative of the following subtypes:

  •  two basel-like TNBC subtypes with cell cycle and DDR gene expression signatures (BL1 and BL2);
  • two mesenchymal subtypes with high expression of genes involved in differentiation and growth factor pathways (M and MSL);
  • an immunomodulatory (IM) type;
  • a luminal subtype driven by androgen signaling (LAR)

The pharmacological drugs were chosen on the basis of the genetic pathways active in the cell lines with the abovementioned TNBC subtypes. It was observed that BL1 and BL2 subtype cell lines respond to cisplatin. Mesenchymal, basal, and luminal subtype lines with aberrations in PI3K signaling and have the greatest sensitivity to PI3K inhibitors.

The LAR subtype cell lines express AR and are uniquely sensitive to bicalutamide (AR antagonist). The experiment was a proof-of-concept that the best therapy could be based on TNBC subtypes.

The group has also developed a web-based subtyping tool referred to as “TNBCtype,” for candidate TNBC tumor samples using our gene expression metadata and classification methods. The approach would enable alignment of TNBC patients to appropriate targeted therapies.

The Clonal and Mutational Composition of Triple Negative Breast Cancers: Aparicio S, University of British Columbia (Vancouver, BC), Canada. Abstract no. ES2-3.

The abstract is on the same lines, TNBC heterogeneity that is. The concept of clonal heterogeneity in cancers, the spatial and temporal variation in clonal composition, is the focal point of the discussion. The group has developed next generation sequencing approaches and applied them to the understanding of mutational and clonal composition of primary TNBC. They have demonstrated that both mutational composition and clonal structure of primary TNBC is in fact a complete spectrum, a notion that is far from the previous one that stated TNBC to be a distinct disease. The authors add “clonal analysis suggests a means by which the genetic complexity might be reduced by following patient evolution over time and space.” The specific implications of the mutational and transcriptome landscapes of TNBC in relation to possible disease biologies were discussed in the symposium.

Profiling of triple-negative breast cancers after neoadjuvant chemotherapy identifies targetable molecular alterations in the treatment-refractory residual disease:

Balko JM, etal, Vanderbilt University (Nashville, TN); Foundation Medicine, (Cambridge, MA); Instituto Nacional de Enfermedades Neoplásicas, Lima, Peru

In the absence of hormone receptors and hence lack of targets, Neoadjuvant chemotherapy (NAC) is increasingly used in patients with TNBC. NAC can induce a pathologic complete response (pCR) in ∼30% of patients which portends a favorable prognosis. In contrast, patients with residual disease (RD) in the breast at surgical resection exhibit worse outcomes. The authors hypothesize that “profiling residual TNBC after NAC would identify molecularly targetable lesions in the chemotherapy resistant component of the tumor and that the persistent tumor cells would mirror micro-metastases which ultimately recur in such patients.” The researchers utilized targeted next generation sequencing (NGS) for 182 oncogenes and tumor suppressors in a CLIA certified lab (Foundation Medicine, Cambridge, MA) and gene expression profiling (NanoString) of the RD after NAC in 102 patients with TNBC. The RD was stained for Ki67, which has been reported to predict outcome after NAC in unselected breast cancers. Out of the 89 evaluable post-NAC tumors, 57 (64%) were basal-like; 19% HER2-enriched; 6% luminal A; 6% luminal B and 5% normal-like. Of 81 tumors evaluated by NGS, 89% demonstrated mutations in TP53, 27% were MCL1-amplified, and 21% were MYC-amplified.

Several pathways were found to be altered:

  • PI3K/mTOR pathway (AKT1-3, PIK3CA, PIK3R1, RAPTOR, PTEN, and TSC1)
  • Cell cycle genes (amplifications of CDK2, CDK4, and CDK6, CCND1-3, and CCNE1); loss of RB
  • DNA repair pathway (BRCA1/2, ATM)
  • Ras/MAPK pathway (KRAS, RAF1, NF1)
  • Sporadic growth factor receptor (amplifications occurred in EGFR, KIT, PDGFRA, PDGFRB, MET, FGFR1, FGFR2, and IGF1R.

NGS identified 7 patients with ERBB2 gene amplification. NGS could assist in the identification of ERBB2-overexpressing tumors misclassified at the time of diagnosis.

Amplifications of MYC were independently associated with poor recurrence-free survival (RFS) and overall survival (OS). In contrast to the earlier notion, high post-NAC Ki67 score did not predict poor RFS or OS in this predominantly TNBC cohort.

The authors concluded that “the diversity of lesions in residual TNBCs after NAC underscores the need for powerful and broad molecular approaches to identify actionable molecular alterations and, in turn, better inform personalized therapy of this aggressive disease.”

Identification of Novel Synthetic-Lethal Targets for MYC-Driven Triple-Negative Breast Cancer: Goga A, etal, UCSF (San Francisco, CA), Abstract No. S3-8

Reiterating the greatest challenge of the TNBC treatment, no targeted agents currently exist against TNBC. The group at UCSF has discovered that TNBC frequently express high levels of the MYC proto-oncogene. The discovery has led them to identify new “synthetic-lethal” strategies to selectively kill TNBC with MYC overexpression. “Synthetic lethality arises when a combination of mutations in two or more genes leads to cell death, whereas a mutation in only one of these genes has little effect. Using this strategy, we can take advantage of the elevated MYC signaling in TNBC to selectively kill them, while sparing normal tissues in which MYC is expressed at much lower levels”

The researchers performed a shRNA synthetic-lethal screen in the human mammary epithelial cells (HMEC), to identify new molecules, such as cell cycle kinases, which when inhibited can preferentially kill TNBC cells. A high-throughput screen of ∼2000 shRNAs, that target the human kinome (∼ 600 kinases) when performed, led to the identification of 13 kinases whose inhibition by >1 shRNAs gave rise to >50% inhibition of cell growth. ARK5 and GSK3A were the two other kinases that were shown to have a synthetic-lethal interaction with MYC. Since these two kinases have been identified in other studies, it gives validity to the ability to the methods of Goga etal in identifying synthetic-lethal targets. The group is currently characterizing and validating the 11 novel targets identified in this screen, using human cancer cell lines as well as mouse cancer models to determine the impact of inhibiting these targets on triple-negative breast cancer development and proliferation.

Reference:

Dent R, etal.  Triple-negative breast cancer: clinical features and patterns of recurrence (2007) Clin Cancer Res 13, 4429-4434.

Lehmann BD, etal. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies (2011) J Clin Invest. 121: 2750-67.

Chen X, etal. TNBCtype: A Subtyping Tool for Triple- Negative Breast Cancer. (2012) Cancer informatics 11, 147-156.

Abstracts presented in SABCS 2012 can be accessed here.

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