Posts Tagged ‘antitumor therapy’

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






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.
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
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
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
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
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.



Similar articles

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.



Free PMC Article

Similar articles

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.



Free PMC Article

Similar articles

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.



Free PMC Article

Similar articles


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.
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

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

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

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.
Tuesday, Apr 21, 2015, 10:30 AM -12:30 PM
David A. Cheresh. UCSD Moores Cancer Center, La Jolla, CA
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
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
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.
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
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.
Tuesday, Apr 21, 2015, 10:30 AM -12:30 PM
Peter B. Dirks. Univ. of Toronto Hospital for Sick Children, Toronto, ON, Canada

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

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

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

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


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?


Read Full Post »

Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer

Curator, Writer: Stephen J. Williams, Ph.D.

UPDATED 10/10/2021

lung cancer

(photo credit: cancer.gov)

A report Lung Cancer Genome Surveys Find Many Potential Drug Targets, in the NCI Bulletin,


summarizes the clinical importance of five new lung cancer genome sequencing projects. These studies have identified genetic and epigenetic alterations in hundreds of lung tumors, of which some alterations could be taken advantage of using currently approved medications.

The reports, all published this month, included genomic information on more than 400 lung tumors. In addition to confirming genetic alterations previously tied to lung cancer, the studies identified other changes that may play a role in the disease.

Collectively, the studies covered the main forms of the disease—lung adenocarcinomas, squamous cell cancers of the lung, and small cell lung cancers.

“All of these studies say that lung cancers are genomically complex and genomically diverse,” said Dr. Matthew Meyerson of Harvard Medical School and the Dana-Farber Cancer Institute, who co-led several of the studies, including a large-scale analysis of squamous cell lung cancer by The Cancer Genome Atlas (TCGA) Research Network.

Some genes, Dr. Meyerson noted, were inactivated through different mechanisms in different tumors. He cautioned that little is known about alterations in DNA sequences that do not encode genes, which is most of the human genome.

Four of the papers are summarized below, with the first described in detail, as the Nature paper used a multi-‘omics strategy to evaluate expression, mutation, and signaling pathway activation in a large cohort of lung tumors. A literature informatics analysis is given for one of the papers.  Please note that links on GENE names usually refer to the GeneCard entry.

Paper 1. Comprehensive genomic characterization of squamous cell lung cancers[1]

The Cancer Genome Atlas Research Network Project just reported, in the journal Nature, the results of their comprehensive profiling of 230 resected lung adenocarcinomas. The multi-center teams employed analyses of

  • microRNA
  • Whole Exome Sequencing including
    • Exome mutation analysis
    • Gene copy number
    • Splicing alteration
  • Methylation
  • Proteomic analysis


Some very interesting overall findings came out of this analysis including:

  • High rates of somatic mutations including activating mutations in common oncogenes
  • Newly described loss of function MGA mutations
  • Sex differences in EGFR and RBM10 mutations
  • driver roles for NF1, MET, ERBB2 and RITI identified in certain tumors
  • differential mutational pattern based on smoking history
  • splicing alterations driven by somatic genomic changes
  • MAPK and PI3K pathway activation identified by proteomics not explained by mutational analysis = UNEXPLAINED MECHANISM of PATHWAY ACTIVATION

however, given the plethora of data, and in light of a similar study results recently released, there appears to be a great need for additional mining of this CGAP dataset. Therefore I attempted to curate some of the findings along with some other recent news relevant to the surprising findings with relation to biomarker analysis.

Makeup of tumor samples

230 lung adenocarcinomas specimens were categorized by:


33% acinar

25% solid

14% micro-papillary

9% papillary

8% unclassified

5% lepidic

4% invasive mucinous

Smoking status

81% of patients reported past of present smoking

The authors note that TCGA samples were combined with previous data for analysis purpose.

A detailed description of Methodology and the location of deposited data are given at the following addresses:

Publication TCGA Web Page: https://tcga-data.nci.nih.gov/docs/publications/luad_2014/

Sequence files: https://cghub.ucsc.edu


Gender and Smoking Habits Show different mutational patterns


WES mutational analysis

  1. a) smoking status

– there was a strong correlations of cytosine to adenine nucleotide transversions with past or present smoking. In fact smoking history separated into transversion high (past and previous smokers) and transversion low (never smokers) groups, corroborating previous results.

mutations in groups              Transversion High                   Transversion Low

TP53, KRAS, STK11,                 EGFR, RB1, PI3CA



  1. b) Gender

Although gender differences in mutational profiles have been reported, the study found minimal number of significantly mutated genes correlated with gender. Notably:

  • EGFR mutations enriched in female cohort
  • RBM10 loss of function mutations enriched in male cohort

Although the study did not analyze the gender differences with smoking patterns, it was noted that RBM10 mutations among males were more prevalent in the transversion high group.

Whole exome Sequencing and copy number analysis reveal Unique, Candidate Driver Genes

Whole exome sequencing revealed that 62% of tumors contained mutations (either point or indel) in known cancer driver genes such as:


However, authors looked at the WES data from the oncogene-negative tumors and found unique mutations not seen in the tumors containing canonical oncogenic mutations.

Unique potential driver mutations were found in

TP53, KEAP1, NF1, and RIT1

The genomics and expression data were backed up by a proteomics analysis of three pathways:

  1. MAPK pathway
  2. mTOR
  3. PI3K pathway

…. showing significant activation of all three pathways HOWEVER the analysis suggested that activation of signaling pathways COULD NOT be deduced from DNA sequencing alone. Phospho-proteomic analysis was required to determine the full extent of pathway modification.

For example, many tumors lacked an obvious mutation which could explain mTOR or MAPK activation.


Altered cell signaling pathways included:

  • Increased MAPK signaling due to activating KRAS
  • Higher mTOR due to inactivating STK11 leading to increased proliferation, translation

Pathway analysis of mutations revealed alterations in multiple cellular pathways including:

  • Reduced oxidative stress response
  • Nucleosome remodeling
  • RNA splicing
  • Cell cycle progression
  • Histone methylation


Authors noted some interesting conclusions including:

  1. MET and ERBB2 amplification and mutations in NF1 and RIT1 may be unique driver events in lung adenocarcinoma
  2. Possible new drug development could be targeted to the RTK/RAS/RAF pathway
  3. MYC pathway as another important target
  4. Cluster analysis using multimodal omics approach identifies tumors based on single-gene driver events while other tumor have multiple driver mutational events (TUMOR HETEROGENEITY)

Paper 2. A Genomics-Based Classification of Human Lung Tumors[2]

The paper can be found at


by The Clinical Lung Cancer Genome Project (CLCGP) and Network Genomic Medicine (NGM),*,

Paper Summary

This sequencing project revealed discrepancies between histologic and genomic classification of lung tumors.


– mutational analysis by whole exome sequencing of 1255 lung tumors of histologically

defined subtypes

– immunohistochemistry performed to verify reclassification of subtypes based on sequencing data


  • 55% of all cases had at least one oncogenic alteration amenable to current personalized treatment approaches
  • Marked differences existed between cluster analysis within and between preclassified histo-subtypes
  • Reassignment based on genomic data eliminated large cell carcinomas
  • Prospective classification of 5145 lung cancers allowed for genomic classification in 75% of patients
  • Identification of EGFR and ALK mutations led to improved outcomes


It is feasible to successfully classify and diagnose lung tumors based on whole exome sequencing data.

Paper 3. Genomic Landscape of Non-Small Cell Lung Cancer in Smokers and Never-Smokers[3]

A link to the paper can be found here with Graphic Summary: http://www.cell.com/cell/abstract/S0092-8674%2812%2901022-7?cc=y?cc=y


  • Whole genome sequencing and transcriptome sequencing of cancerous and adjacent normal tissues from 17 patients with NSCLC
  • Integrated RNASeq with WES for analysis of
    • Variant analysis
    • Clonality by variant allele frequency anlaysis
    • Fusion genes
  • Bioinformatic analysis


  • 3,726 point mutations and more than 90 indels in the coding sequence
  • Smokers with lung cancer show 10× the number of point mutations than never-smokers
  • Novel lung cancer genes, including DACH1, CFTR, RELN, ABCB5, and HGF were identified
  • Tumor samples from males showed high frequency of MYCBP2 MYCBP2 involved in transcriptional regulation of MYC.
  • Variant allele frequency analysis revealed 10/17 tumors were at least biclonal while 7/17 tumors were monoclonal revealing majority of tumors displayed tumor heterogeneity
  • Novel pathway alterations in lung cancer include cell-cycle and JAK-STAT pathways
  • 14 fusion proteins found, including ROS1-ALK fusion. ROS1-ALK fusions have been frequently found in lung cancer and is indicative of poor prognosis[4].
  • Novel metabolic enzyme fusions
  • Alterations were identified in 54 genes for which targeted drugs are available.           Drug-gable mutant targets include: AURKC, BRAF, HGF, EGFR, ERBB4, FGFR1, MET, JAK2, JAK3, HDAC2, HDAC6, HDAC9, BIRC6, ITGB1, ITGB3, MMP2, PRKCB, PIK3CG, TERT, KRAS, MMP14

Table. Validated Gene-Fusions Obtained from Ref-Seq Data

Note: Gene columns contain links for GeneCard while Gene function links are to the    gene’s GO (Gene Ontology) function.

GeneA (5′) GeneB (3′) GeneA function (link to Gene Ontology) GeneB function (link to Gene Ontology) known function (refs)
GRIP1 TNIP1 glutamate receptor IP transcriptional repressor
SGMS1 STK10 sphingolipid synthesis ser/thr kinase
RASSF3 TTYH2 GTP-binding protein chloride anion channel
KDELR2 ROS1, GOPC ER retention seq. binding proto-oncogenic tyr kinase
ACSL4 DCAF6 fatty acid synthesis ?
MARCH8 PRKG1 ubiquitin ligase cGMP dependent protein kinase
APAF1 UNC13B, TLN1 caspase activation cytoskeletal
EML4 ALK microtubule protein tyrosine kinase
EDR3,PHC3 LOC441601 polycomb pr/DNA binding ?
DKFZp761L1918,RHPN2 ANKRD27 Rhophilin (GTP binding pr ankyrin like
VANGL1 HAO2 tetraspanin family oxidase
CACNA2D3 FLNB VOC Ca++ channel filamin (actin binding)

Author’s Note:

There has been a recent literature on the importance of the EML4-ALK fusion protein in lung cancer. EML4-ALK positive lung tumors were found to be les chemo sensitive to cytotoxic therapy[5] and these tumor cells may exhibit an epitope rendering these tumors amenable to immunotherapy[6]. In addition, inhibition of the PI3K pathway has sensitized EMl4-ALK fusion positive tumors to ALK-targeted therapy[7]. EML4-ALK fusion positive tumors show dependence on the HSP90 chaperone, suggesting this cohort of patients might benefit from the new HSP90 inhibitors recently being developed[8].

Table. Significantly mutated genes (point mutations, insertions/deletions) with associated function.

Gene Function
TP53 tumor suppressor
KRAS oncogene
ZFHX4 zinc finger DNA binding
DACH1 transcription factor
EGFR epidermal growth factor receptor
EPHA3 receptor tyrosine kinase
RELN cell matrix protein
ABCB5 ABC Drug Transporter

Table. Literature Analysis of pathways containing significantly altered genes in NSCLC reveal putative targets and risk factors, linkage between other tumor types, and research areas for further investigation.

Note: Significantly mutated genes, obtained from WES, were subjected to pathway analysis (KEGG Pathway Analysis) in order to see which pathways contained signicantly altered gene networks. This pathway term was then used for PubMed literature search together with terms “lung cancer”, “gene”, and “NOT review” to determine frequency of literature coverage for each pathway in lung cancer. Links are to the PubMEd search results.

KEGG pathway Name # of PUBMed entries containing Pathway Name, Gene ANDLung Cancer
Cell cycle 1237
Cell adhesion molecules (CAMs) 372
Glioma 294
Melanoma 219
Colorectal cancer 207
Calcium signaling pathway 175
Prostate cancer 166
MAPK signaling pathway 162
Pancreatic cancer 88
Bladder cancer 74
Renal cell carcinoma 68
Focal adhesion 63
Regulation of actin cytoskeleton 34
Thyroid cancer 32
Salivary secretion 19
Jak-STAT signaling pathway 16
Natural killer cell mediated cytotoxicity 11
Gap junction 11
Endometrial cancer 11
Long-term depression 9
Axon guidance 8
Cytokine-cytokine receptor interaction 8
Chronic myeloid leukemia 7
ErbB signaling pathway 7
Arginine and proline metabolism 6
Maturity onset diabetes of the young 6
Neuroactive ligand-receptor interaction 4
Aldosterone-regulated sodium reabsorption 2
Systemic lupus erythematosus 2
Olfactory transduction 1
Huntington’s disease 1
Chemokine signaling pathway 1
Cardiac muscle contraction 1
Amyotrophic lateral sclerosis (ALS) 1

A few interesting genetic risk factors and possible additional targets for NSCLC were deduced from analysis of the above table of literature including HIF1-α, mIR-31, UBQLN1, ACE, mIR-193a, SRSF1. In addition, glioma, melanoma, colorectal, and prostate and lung cancer share many validated mutations, and possibly similar tumor driver mutations.


 please click on graph for larger view

Paper 4. Mapping the Hallmarks of Lung Adenocarcinoma with Massively Parallel Sequencing[9]

For full paper and graphical summary please follow the link: http://www.cell.com/cell/abstract/S0092-8674%2812%2901061-6


  • Exome and genome characterization of somatic alterations in 183 lung adenocarcinomas
  • 12 somatic mutations/megabase
  • U2AF1, RBM10, and ARID1A are among newly identified recurrently mutated genes
  • Structural variants include activating in-frame fusion of EGFR
  • Epigenetic and RNA deregulation proposed as a potential lung adenocarcinoma hallmark


Lung adenocarcinoma, the most common subtype of non-small cell lung cancer, is responsible for more than 500,000 deaths per year worldwide. Here, we report exome and genome sequences of 183 lung adenocarcinoma tumor/normal DNA pairs. These analyses revealed a mean exonic somatic mutation rate of 12.0 events/megabase and identified the majority of genes previously reported as significantly mutated in lung adenocarcinoma. In addition, we identified statistically recurrent somatic mutations in the splicing factor gene U2AF1 and truncating mutations affecting RBM10 and ARID1A. Analysis of nucleotide context-specific mutation signatures grouped the sample set into distinct clusters that correlated with smoking history and alterations of reported lung adenocarcinoma genes. Whole-genome sequence analysis revealed frequent structural rearrangements, including in-frame exonic alterations within EGFR and SIK2 kinases. The candidate genes identified in this study are attractive targets for biological characterization and therapeutic targeting of lung adenocarcinoma.

Paper 5. Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer[10]


  • Whole exome and transcriptome (RNASeq) sequencing 29 small-cell lung carcinomas
  • High mutation rate 7.4 protein-changing mutations/million base pairs
  • Inactivating mutations in TP53 and RB1
  • Functional mutations in CREBBP, EP300, MLL, PTEN, SLIT2, EPHA7, FGFR1 (determined by literature and database mining)
  • The mutational spectrum seen in human data also present in a Tp53-/- Rb1-/- mouse lung tumor model


Curator Graphical Summary of Interesting Findings From the Above Studies


The above figure (please click on figure) represents themes and findings resulting from the aforementioned studies including

questions which will be addressed in Future Posts on this site.

UPDATED 10/10/2021

The following article uses RNASeq to screen lung adenocarcinomas for fusion proteins in patients with either low or high tumor mutational burden. Findings included presence of MET fusion proteins in addition to other fusion proteins irrespective if tumors were driver negative by DNASeq screening.

High Yield of RNA Sequencing for Targetable Kinase Fusions in Lung Adenocarcinomas with No Mitogenic Driver Alteration Detected by DNA Sequencing and Low Tumor Mutation Burden


High Yield of RNA Sequencing for Targetable Kinase Fusions in Lung Adenocarcinomas with No Mitogenic Driver Alteration Detected by DNA Sequencing and Low Tumor Mutation Burden
Ryma BenayedMichael OffinKerry MullaneyPurvil SukhadiaKelly RiosPatrice DesmeulesRyan PtashkinHelen WonJason ChangDarragh HalpennyAlison M. SchramCharles M. RudinDavid M. HymanMaria E. ArcilaMichael F. BergerAhmet ZehirMark G. KrisAlexander Drilon and Marc Ladanyi


Purpose: Targeted next-generation sequencing of DNA has become more widely used in the management of patients with lung adenocarcinoma; however, no clear mitogenic driver alteration is found in some cases. We evaluated the incremental benefit of targeted RNA sequencing (RNAseq) in the identification of gene fusions and MET exon 14 (METex14) alterations in DNA sequencing (DNAseq) driver–negative lung cancers.

Experimental Design: Lung cancers driver negative by MSK-IMPACT underwent further analysis using a custom RNAseq panel (MSK-Fusion). Tumor mutation burden (TMB) was assessed as a potential prioritization criterion for targeted RNAseq.

Results: As part of prospective clinical genomic testing, we profiled 2,522 lung adenocarcinomas using MSK-IMPACT, which identified 195 (7.7%) fusions and 119 (4.7%) METex14 alterations. Among 275 driver-negative cases with available tissue, 254 (92%) had sufficient material for RNAseq. A previously undetected alteration was identified in 14% (36/254) of cases, 33 of which were actionable (27 in-frame fusions, 6 METex14). Of these 33 patients, 10 then received matched targeted therapy, which achieved clinical benefit in 8 (80%). In the 32% (81/254) of DNAseq driver–negative cases with low TMB [0–5 mutations/Megabase (mut/Mb)], 25 (31%) were positive for previously undetected gene fusions on RNAseq, whereas, in 151 cases with TMB >5 mut/Mb, only 7% were positive for fusions (P < 0.0001).

Conclusions: Targeted RNAseq assays should be used in all cases that appear driver negative by DNAseq assays to ensure comprehensive detection of actionable gene rearrangements. Furthermore, we observed a significant enrichment for fusions in DNAseq driver–negative samples with low TMB, supporting the prioritization of such cases for additional RNAseq.

Translational Relevance

Inhibitors targeting kinase fusions have shown dramatic and durable responses in lung cancer patients, making their comprehensive detection critical. Here, we evaluated the incremental benefit of targeted RNA sequencing (RNAseq) in the identification of gene fusions in patients where no clear mitogenic driver alteration is found by DNA sequencing (DNAseq)–based panel testing. We found actionable alterations (kinase fusions or MET exon 14 skipping) in 13% of cases apparently driver negative by previous DNAseq testing. Among the driver-negative samples tested by RNAseq, those with low tumor mutation burden (TMB) were significantly enriched for gene fusions when compared with the ones with higher TMB. In a clinical setting, such patients should be prioritized for RNAseq. Thus, a rational, algorithmic approach to the use of targeted RNA-based next-generation sequencing (NGS) to complement large panel DNA-based NGS testing can be highly effective in comprehensively uncovering targetable gene fusions or oncogenic isoforms not just in lung cancer but also more generally across different tumor types.

A Commentary is in the same issue at https://clincancerres.aacrjournals.org/content/25/15/4586?iss=15

Wake Up and Smell the Fusions: Single-Modality Molecular Testing Misses Drivers

by Kurtis D. Davies and Dara L. Aisner


Multitarget assays have become common in clinical molecular diagnostic laboratories. However, all assays, no matter how well designed, have inherent gaps due to technical and biological limitations. In some clinical cases, testing by multiple methodologies is needed to address these gaps and ensure the most accurate molecular diagnoses.

See related article by Benayed et al., p. 4712

In this issue of Clinical Cancer Research, Benayed and colleagues illustrate the growing need to consider multiple molecular testing methodologies for certain clinical specimens (1). The rapidly expanding list of actionable molecular alterations across cancer types has resulted in the wide adoption of multitarget testing approaches, particularly those based on next-generation sequencing (NGS). NGS-based assays are commonly viewed as “one-stop shops” to detect a vast array of molecular variants. However, as Benayed and colleagues discuss, even well-designed and highly vetted NGS assays have inherent gaps that, under certain circumstances, are ideally addressed by analyzing the sample using an alternative approach.

In the article, the authors examined a cohort of lung adenocarcinoma patient samples that had been deemed “driver- negative” via MSK-IMPACT, an FDA-cleared test that is widely considered by experts in the field to be one of the best examples of a DNA-based large gene panel NGS assay (2). Of 589 driver-negative cases, 254 had additional material amenable for a different approach: RNA-based NGS designed specifically for gene fusion and oncogenic gene isoform detection. After accounting for quality control failures, 232 samples were successfully sequenced, and, among these, 36 samples (representing an astonishing 15.5% of tested cases) were found to be positive for a driver gene fusion or oncogenic isoform that had not been detected by DNA-based NGS. The real-world value derived from this orthogonal testing schema was more than theoretical, with 8 of 10 (80%) patients demonstrating clinical benefit when treated according to the alteration identified via the RNA-based approach.

To detect gene rearrangements that lead to oncogenic gene fusions (and to detect mutations and insertions/deletions that lead to MET exon 14 skipping), MSK-IMPACT employs hybrid capture-based enrichment of selected intronic regions from genomic DNA. While this approach has proven to be successful in a variety of settings, there are associated limitations that were determined in this study to underlie the discrepancies between MSK-IMPACT and the RNA-based assay. First, some introns that are involved in clinically actionable rearrangement events are very large, thus requiring substantial sequencing capital that can represent a disproportionate fraction of the assay. Despite the ability via NGS to perform sequencing at a large scale, this sequencing capacity is still finite, and thus decisions must be made to sacrifice coverage of certain large genomic regions to ensure sufficient sequencing depth for other desired genomic targets. In the case of MSK-IMPACT (and most other DNA-based NGS assays), certain important introns in NTRK3 and NRG1 are not included in covered content, simply because they are too large (>90 Kb each). The second primary problem with DNA-based analysis of introns is that they often contain highly repetitive elements that are extremely difficult to assess via NGS due to their recurring presence across the genome. Attempts to sequence these regions are largely unfruitful because any sequencing data obtained cannot be specifically aligned/mapped to the desired targeted region of the genome (3). This is particularly true for intron 31 of ROS1, because it contains two repetitive long interspersed nuclear elements, and many DNA-based assays, including MSK-IMPACT, poorly cover this intron (4). In this study by Benayed and colleagues, the most common discrepant alteration was fusion involving ROS1, which accounted for 10 of 36 (28%) cases. At least six of these, those that demonstrated fusion to ROS1 exon 32, were likely directly explained by incomplete intron 31 sequencing. RNA-based analysis is able to overcome the above described limitations owing to the simple fact that sequencing is focused on exons post-splicing and the need to sequence introns is entirely avoided (Fig. 1).

Figure 1.

Schematic representation of underlying genomic complexities that can lead to false-negative gene fusion results in DNA-based NGS analysis. In some cases, RNA-based approaches may overcome the limitations of DNA-based testing.

Lack of sufficient intronic coverage could not account for all of the discrepancies between DNA-based and RNA-based analysis however. Six samples in the cohort were found to be positive for MET exon 14 skipping based on RNA. In five of these, genomic alterations in MET introns 13 or 14 were observed, however they did not conform to canonical splice site alterations and thus were not initially called (although this was addressed by bioinformatics updates). In RNA-based testing, however, determination of exon skipping is simplified such that, regardless of the specific genomic alteration that interferes with splicing, absence of the exon in the transcript is directly observed (5). In another two of the discrepant cases, tumor purity was observed to be low in the sample, meaning that the expected variant allele frequency (VAF) for a genomic event would also likely be low, potentially below detectable levels. However, overexpression of the fusions at the transcript level was theorized to compensate for low VAF (Fig. 1). Additional explanations for discordant findings between the assays included sample-specific poor sequencing in selected introns and complex rearrangements that hindered proper capture (Fig. 1).

The take home message from Benayed and colleagues is simply this: there is no perfect assay that will detect 100% of the potential actionable alterations in patient samples. Even an extremely well designed, thoroughly vetted, and FDA-cleared assay such as MSK-IMPACT will have inherent and unavoidable “holes” due to intrinsic limitations. The solution to this dilemma, as adeptly described by Benayed and colleagues, is additional testing using a different approach. While in an ideal world every clinical tumor sample would be tested by multiple modalities to ensure the most comprehensive clinical assessment, the reality is that these samples are often scant and testing is fiscally burdensome (and often not reimbursed). Therefore, algorithms to determine which samples should be reflexed to secondary assays after testing with a primary assay are critical for maximizing benefit. In this study, the first algorithmic step was lack of an identified driver (because activated oncogenic drivers tend to exist exclusively of each other), which amounted to 23% of samples tested with the primary assay. In addition, the authors found a significantly higher rate of actionable gene fusions in samples with a low (<5 mut/Mb) tumor mutational burden, meaning that this metric, which was derived from the primary assay, could also be used to help inform decision making regarding additional testing. While this scenario is somewhat specific to lung cancer, similar approaches could be prescribed on a cancer type–specific basis.

These findings should be considered a “wake-up call” for oncologists in regard to the ordering and interpretation of molecular testing. It is clear from these and other published findings that advanced molecular analysis has limitations that require nuanced technical understanding. As this arena evolves, it is critical for oncologists (and trainees) to gain an increased comprehension of how to identify when the “gaps” in a test might be most clinically relevant. This requires a level of technical cognizance that has been previously unexpected of clinical practitioners, yet is underscored by the reality that opportunities for effective targeted therapy can and will be missed if the treating oncologist is unaware of how to best identify patients for whom additional testing is warranted. This study also highlights the mantra of “no test is perfect” regardless of prestige of the testing institution, number of past tests performed, or regulatory status. NGS, despite its benefits, does not mean all-encompassing. It is only through the adaptability of laboratories to utilize knowledge such as is provided by Benayed and colleagues that advances in laboratory medicine can be quickly deployed to maximize benefits for oncology patients.


  1. Comprehensive genomic characterization of squamous cell lung cancers. Nature 2012, 489(7417):519-525.
  2. A genomics-based classification of human lung tumors. Science translational medicine 2013, 5(209):209ra153.
  3. Govindan R, Ding L, Griffith M, Subramanian J, Dees ND, Kanchi KL, Maher CA, Fulton R, Fulton L, Wallis J et al: Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 2012, 150(6):1121-1134.
  4. Takeuchi K, Soda M, Togashi Y, Suzuki R, Sakata S, Hatano S, Asaka R, Hamanaka W, Ninomiya H, Uehara H et al: RET, ROS1 and ALK fusions in lung cancer. Nature medicine 2012, 18(3):378-381.
  5. Morodomi Y, Takenoyama M, Inamasu E, Toyozawa R, Kojo M, Toyokawa G, Shiraishi Y, Takenaka T, Hirai F, Yamaguchi M et al: Non-small cell lung cancer patients with EML4-ALK fusion gene are insensitive to cytotoxic chemotherapy. Anticancer research 2014, 34(7):3825-3830.
  6. Yoshimura M, Tada Y, Ofuzi K, Yamamoto M, Nakatsura T: Identification of a novel HLA-A 02:01-restricted cytotoxic T lymphocyte epitope derived from the EML4-ALK fusion gene. Oncology reports 2014, 32(1):33-39.
  7. Yang L, Li G, Zhao L, Pan F, Qiang J, Han S: Blocking the PI3K pathway enhances the efficacy of ALK-targeted therapy in EML4-ALK-positive nonsmall-cell lung cancer. Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine 2014.
  8. Workman P, van Montfort R: EML4-ALK fusions: propelling cancer but creating exploitable chaperone dependence. Cancer discovery 2014, 4(6):642-645.
  9. Imielinski M, Berger AH, Hammerman PS, Hernandez B, Pugh TJ, Hodis E, Cho J, Suh J, Capelletti M, Sivachenko A et al: Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 2012, 150(6):1107-1120.
  10. Peifer M, Fernandez-Cuesta L, Sos ML, George J, Seidel D, Kasper LH, Plenker D, Leenders F, Sun R, Zander T et al: Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nature genetics 2012, 44(10):1104-1110.

Other posts on this site which refer to Lung Cancer and Cancer Genome Sequencing include:

Multi-drug, Multi-arm, Biomarker-driven Clinical Trial for patients with Squamous Cell Carcinoma called the Lung Cancer Master Protocol, or Lung-MAP launched by NCI, Foundation Medicine, and Five Pharma Firms

US Personalized Cancer Genome Sequencing Market Outlook 2018 –

Comprehensive Genomic Characterization of Squamous Cell Lung Cancers

International Cancer Genome Consortium Website has 71 Committed Cancer Genome Projects Ongoing

Non-small Cell Lung Cancer drugs – where does the Future lie?

Lung cancer breathalyzer trialed in the UK

Diagnosing Lung Cancer in Exhaled Breath using Gold Nanoparticles

Multi-drug, Multi-arm, Biomarker-driven Clinical Trial for patients with Squamous Cell Carcinoma called the Lung Cancer Master Protocol, or Lung-MAP launched by NCI, Foundation Medicine, and Five Pharma Firms

Read Full Post »

Larry H Bernstein, MD, FCAP

http://pharmaceuticalintelligence.com/6-19-2014/larryhbern/Gene Switch Takes Blood Cells to Leukemia and Back Again

Kevin Mayer



Loss-of-function mutations in a gene called Pax5 have been known to drive normal blood cells to turn into leukemia cells. Such mutations are permanent, so it remained unclear whether an initial, temporary loss of function would instigate an irreversible cascade of events leading to an accumulation of undifferentiated lymphoblasts, or whether an ongoing loss of function would be needed to maintain the disease state.


With the publication of a new study, the question has become more than academic. The study, by researchers at Melbourne’s Walter and Eliza Hall Institute, has not only shown that switching off Pax5 causes cancer in a murine model of B-progenitor acute lymphoblastic leukemia (B-ALL), it has also demonstrated that switching on Pax5 essentially cures the disease.

The results of the study appeared June 15 in the journal Genes & Development, in an article entitled “Pax5 loss imposes a reversible differentiation block in B-progenitor acute lymphoblastic leukemia.” The article described how the researchers used transgenic RNAi to reversibly suppress endogenous Pax5 expression in the hematopoietic compartment of mice, which cooperates with activated signal transducer and activator of transcription 5 (STAT5) to induce B-ALL.

“In this model, restoring endogenous Pax5 expression in established B-ALL triggers immunophenotypic maturation and durable disease remission by engaging a transcriptional program reminiscent of normal B-cell differentiation,” wrote the authors. “Notably, even brief Pax5 restoration in B-ALL cells causes rapid cell cycle exit and disables their leukemia-initiating capacity.”

Institute researcher Grace Liu noted that Pax5, which is frequently “lost” in childhood B-ALL, is essential for normal development of B cells. “When Pax5 function is compromised, developing B cells can get trapped in an immature state and become cancerous,” she said. “We have shown that restoring Pax5 function, even in cells that have already become cancerous, removes this ‘block,’ and enables the cells to develop into normal white blood cells.”

Simply restoring Pax5 sufficed to normalize cancer cells. That is, re-engaging the stalled differentiation program in immature white blood cells restored normal development “despite the presence of additional oncogenic lesions.”

Institute researcher Ross Dickins, Ph.D., said that forcing B-ALL cells to resume their normal development could provide a new strategy for treating leukemia: “While B-ALL has a relatively good prognosis compared with other cancers, current treatments can last years and have major side effects. By understanding how specific genetic changes drive B-ALL, it may be possible to develop more specific treatments that act faster with fewer side effects.”

“It is very difficult to develop drugs that restore the function of genes that are lost during cancer development,” Dr. Dickins added. “However, by understanding the mechanisms by which Pax5 loss causes leukemia, we can begin to look at ways of developing drugs that could have the same effect as restoring Pax5 function.”

Pax5 is just one of about 100 genes known to suppress human tumors. Now that Pax5 has been scrutinized with genetic switch technology, the researchers speculate that similar technology could be used to characterize other tumor suppressor genes.


Read Full Post »

Author and curator: Ritu Saxena, Ph.D.

This post attempts to integrate three posts and to embed all comments made to all three papers, allowing the reader a critically thought compilation of evidence-based medicine and scientific discourse.

Dr. Dror Nir authored a post on October 16th titled “Knowing the tumor’s size and location, could we target treatment to THE ROI by applying imaging-guided intervention?” The article attracted over 20 comments from readers including researchers and oncologists debating the following issues:

  • imaging technologies in cancer
  • tumor size, and
  • tumor response to treatment.

The debate lead to several new posts authored by:

This post is a compilation of the views of authors representing different specialties including research and medicine. In medicine: Pathology, Oncology Surgery and Medical Imaging, are represented.

Dr. Nir’s post talked about an advanced technique developed by the researchers at Sunnybrook Health Sciences Centre, University of Toronto, Canada for cancer lesions’ detection and image-guided cancer treatment in the specific Region of Interest (ROI). The group was successfully able to show the feasibility and safety of magnetic resonance imaging (MRI) – controlled transurethral ultrasound therapy for prostate cancer in eight patients.

The dilemma of defining the Region of Interest for imaging-based therapy

Dr. Bernstein, one of the authors at Pharmaceuticalintelligence.com, a Fellow of the American College of Pathology, reiterated the objective of the study stating that “Their study’s objective was to prove that using real-time MRI guidance of HIFU treatment is possible and it guarantees that the location of ablated tissue indeed corresponds to the locations planned for treatment.” He expressed his opinion about the study by bringing into focus a very important issue i.e., given the fact that the part surrounding the cancer tissue is in the transition state, challenge in defining a ROI that could be approached by imaging-based therapy. Regarding the study discussed, he states – “This is a method demonstration, but not a proof of concept by any means.  It adds to the cacophany of approaches, and in a much larger study would prove to be beneficial in treatment, but not a cure for serious prostate cancer because it is unlikely that it can get beyond the margin, and also because there is overtreatment at the cutoff of PSA at 4.0. I think that the pathologist has to see the tissue, and the standard in pathology now is for any result that is cancer, two pathologists or a group sitting together should see it. It’s not an easy diagnosis.”

“The crux of the matter in terms of capability is that the cancer tissue, adjacent tissue, and the fibrous matrix are all in transition to the cancerous state. It is taught to resect leaving “free margin”, which is better aesthetically, and has had success in breast surgery. The dilemma is that the patient may return, but how soon?” concludes Dr. Larry.

Dr. Nir responded, “The philosophy behind lumpectomy is preserving quality of life. It was Prof. Veronesi (IEO) who introduced this method 30 years ago noticing that in the majority of cases; the patient will die from something else before presenting recurrence of breast cancer. It is well established that when the resection margins are declared by a pathologist (as good as he/she could be) as “free of cancer”, the probability of recurrence is much lower than otherwise. He explains further, “The worst enemy of finding solutions is doing nothing while using the excuse of looking for the “ultimate solution.” Personally, I believe in combining methods and improving clinical assessment based on information fusion. Being able to predict, and then timely track the response to treatment is a major issue that affects survival and costs!

In this discussion my view is expressed, below.

  • The paper that discusses imaging technique had the objective of finding out whether real-time MRI guidance of treatment was even possible and if yes, whether the treatment could be performed in accurate location of the ROI? The data reveals they were pretty successful in accomplishing their objective and of course that gives hope to the imaging-based targeted therapies.
  • Whether the ROI is defined properly and if it accounts for the real tumor cure, is a different question. Role of pathologists and the histological analysis and what they bring to the table cannot be ruled out, and the absence of a defined line between the tumor and the stromal region in the vicinity is well documented. However, that cannot rule out the value and scope of imaging-based detection and targeted therapy. After all, it is seminal in guiding minimally invasive surgery.
  • As another arm of personalized medicine-based cure for cancer, molecular biologists at MD Anderson have suggested molecular and genetic profiling of the tumor to determine genetic aberrations on the basis of which matched-therapy could be recommended to patients.
  • When phase I trial was conducted, the results were encouraging and the survival rate was better in matched-therapy patients compared to unmatched patients. Therefore, every time there is more to consider when treating a cancer patient and who knows a combination of views of oncologists, pathologists, molecular biologists, geneticists, surgeons would device improvised protocols for diagnosis and treatment. It is always going to be complicated and generalizations would never give an answer. Smart interpretations of therapies – imaging-based or others would always be required!

To read additional comments, including those from Dr. Williams, Dr. Lev-Ari, refers to:

Knowing the tumor’s size and location, could we target treatment to THE ROI by applying imaging-guided intervention? Author and Reporter: Dror Nir, Ph.D.

Dr. Lev-Ari in her paper linked three fields that bear weight in the determination of Tumor Response to Therapy:

  • Personalized Medicine
  • Cancer Cell Biology, and
  • Minimally Invasive Surgery (MIS)

Her objectives were to address research methodology, the heterogeneity innate to Cancer Cell Biology and Treatment choice in the Operating Room — all are related to the topic at hand: How to deliver optimal care with least invasive intervention course.

Any attempt aimed at approaching this desirable result, called Personalized Medicine,  involves engagement in three strategies:

  • prediction of Patient’s reaction to Drug induction
  • design of Clinical Trials to validate drug efficacy on small subset of patients predicted to react favorable to drug regimen, increasing validity and reliability
  • Genetical identification of patients at no need to have a drug administered if non sensitivity to the drug has been predicted

These method are to be applied to a list of 56 leading Cancer types.

While the executive task of the clinician remains to assess the differentiation in Tumor Response to Treatment, pursuit of  individualized histopathology, as well as tumor molecular, genetic and functional characteristics has to take into consideration the “total” individual patients’ characteristics: age, co-morbidities, secondary risks and allergies to drugs.

In Dr. Lev-Ari’s paper Minimally Invasive Treatment (MIT) is compared with Minimally Invasive Surgery (MIS) applied for tumor resection.  In many cases MIS is not the right surgical decision, yet, it is applied for a corollary of patient-centered care considerations. At present, facing the unknown of the future behavior of the tumor as its response to therapeutics bearing uncertainty related to therapy outcomes.

Forget me not – says the ‘Stroma’

Dr. Brücher, the author of review on tumor response criteria, expressed his views on the topic. He remembers that 10 years ago, every cancer researcher stated – “look at the tumor cells only – forget the stroma”. However, the times have changed, “now, everyone knows that it is a system we are looking at, and viewing and analyzing only tumor cells is really not enough.”

He went on to state “if we would be honest, we would have to declare that all data, which had been produced 8-13 years ago, dealing with laser capture microdissection, would need a rescrutinization, because the influence of the stroma was ‘forgotten’.”

He added, “the surgeon looks at the ‘free margin’ in a kind of reductionable model, the pathologist is more the control instance. I personally see the pathologist as ‘the control instance’ of surgical quality. Therefore, not the wish of the surgeon is important, the objective way of looking into problems or challenges. Can a pathologist always state if a R0-resection had been performed?”

What is the real RO-resection?

There have been many surrogate marker analysis, says Dr. Brücher, and that a substantially well thought through structured analysis has never been done: mm by mm and afterwards analyzing that by a ROC analysis. For information on genetic markers on cancer, refer to the following post by Dr. Lev-Ari’s: Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS)

He also stated that there is no gold standard to compare the statistical ROC analysis to. Often it is just declared and stated but it is still not clear what the real RO-resection is?

He added, “in some organs it is very difficult and we all (surgeons, pathologists, clinicians) that we always get to the limit, if we try interpreting the R-classification within the 3rd dimension.”

Dr. Brücher explains regarding resectability classification, “If lymph nodes are negative it does not mean, lymph nodes are really negative. For example, up to 38% upper GI cancers have histological negative lymph nodes, but immunohistochemical positive lymph nodes. And, Stojadinovic et al have also shown similar observations at el in colorectal cancer. So the 4th dimension of cancer – the lymph nodes / the lymphatic vessel invasion are much more important than just a TNM classification, which unfortunately does often not reflect real tumor biology.”

The discussion regarding the transition state of the tumor surrounding tissue and the ‘free margin’ led to a bigger issue, the heterogeneity of tumors.

Dr. Bernstein quoted a few lines from the review article titled “Tumor response criteria: are they appropriate?, authored by Dr Björn LDM Brücher et al published in Future Oncology in 2012.

  • Tumor heterogeneity is a ubiquitous phemomenon. In particular, there are important differences among the various types of gastrointestinal (GI) cancers in terms of tumor biology, treatment response and prognosis.
  • This forms the principal basis for targeted therapy directed by tumor-specific testing at either the gene or protein level. Despite rapid advances in our understanding of targeted therapy for GI cancers, the impact on cancer survival has been marginal.
  • Can tumor response to therapy be predicted, thereby improving the selection of patients for cancer treatment?
  • In 2000, the NCI with the European Association for Research and Treatment of Cancer, proposed a replacement of 2D measurement with a decrease in the largest tumor diameter by 30% in one dimension. Tumor response as defined would translate into a 50% decrease for a spherical lesion
  • We must rethink how we may better determine treatment response in a reliable, reproducible way that is aimed at individualizing the therapy of cancer patients.
  • We must change the tools we use to assess tumor response. The new modality should be based on empirical evidence that translates into relevant and meaningful clinical outcome data.
  • This becomes a conundrum of sorts in an era of ‘minimally invasive treatment’.
  • Integrated multidisciplinary panel of international experts – not sure that that will do it.

Dr. Bernstein followed up by authoring a separate post on tumor response. His views on tumor response criteria have been quoted in the following paragraphs:

Can tumor response to therapy be predicted?

The goal is not just complete response. Histopathological response seems to be related post-treatment histopathological assessment but it is not free from the challenge of accurately determining treatment response, as this method cannot delineate whether or not there are residual cancer cells. Functional imaging to assess metabolic response by 18-fluorodeoxyglucose PET also has its limits, as the results are impacted significantly by several variables:

• tumor type
• sizing
• doubling time
• anaplasia?
• extent of tumor necrosis
• type of antitumor therapy and the time when response was determined.

The new modality should be based on individualized histopathology as well as tumor molecular, genetic and functional characteristics, and individual patients’ characteristics, a greater challenge in an era of ‘minimally invasive treatment’.

This listing suggests that for every cancer the following data has to be collected (except doubling time). If there were five variables, the classification based on these alone would calculate to be very sizable based on Eugene Rypka’s feature extraction and classification.

But looking forward, time to remission and disease free survival are additionally important. Treatment for cure is not the endpoint, but the best that can be done is to extend the time of survival to a realistic long term goal and retain a quality of life.

For detailed discussion on the topic of tumor response and comments refer to the following posts:

What can we expect of tumor therapeutic response?

Author: Larry H. Bernstein, MD, FCAP

Judging ‘Tumor response’-there is more food for thought

Reporter: Ritu Saxena, Ph.D.

Additional Sources:

Research articles:

Brücher BLDM  et al. Tumor response criteria: are they appropriate? Future Oncol. August Vol. 8, No. 8, Pages 903-906 (2012).

Brücher BLDM, Piso P, Verwaal V et al. Peritoneal carcinomatosis: overview and basics. Cancer Invest.30(3),209–224 (2012).

Brücher BLDM, Swisher S, Königsrainer A et al. Response to preoperative therapy in upper gastrointestinal cancers. Ann. Surg. Oncol.16(4),878–886 (2009).

Miller AB, Hoogstraten B, Staquet M, Winkler A. Reporting results of cancer treatment. Cancer47(1),207–214 (1981).

Therasse P, Arbuck SG, Eisenhauer EA et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J. Natl Cancer Inst.92(3),205–216 (2000).

Brücher BLDM, Becker K, Lordick F et al. The clinical impact of histopathological response assessment by residual tumor cell quantification in esophageal squamous cell carcinomas. Cancer106(10),2119–2127 (2006).

Read Full Post »

%d bloggers like this: