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
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PresentationNumber:NGO2 |
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| 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. |
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| 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. | |
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
Select item 253494242.
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.
PMID:
25349424
Select item 238053823.
Evolutionary dynamics of cancer in response to targeted combination therapy.
Bozic I, Reiter JG, Allen B, Antal T, Chatterjee K, Shah P, Moon YS, Yaqubie A, Kelly N, Le DT, Lipson EJ, Chapman PB, Diaz LA Jr, Vogelstein B, Nowak MA.
Elife. 2013 Jun 25;2:e00747. doi: 10.7554/eLife.00747.
PMID:
23805382
Select item 227228434.
The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers.
Diaz LA Jr, Williams RT, Wu J, Kinde I, Hecht JR, Berlin J, Allen B, Bozic I, Reiter JG, Nowak MA, Kinzler KW, Oliner KS, Vogelstein B.
Nature. 2012 Jun 28;486(7404):537-40. doi: 10.1038/nature11219.
PMID:
22722843
| 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.
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| 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. |
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| 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. | |||||||||||||||||||||||||||||||||||||||||||||||
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| 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. | ||||||||||||||||||||
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| 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 |
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| 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 |
| 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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