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

Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing[1]

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

UPDATED 3/11/2023

The updates to this article are an expansion of the original article on 4/10/2013 on tumor heterogeneity in renal cancer, extended to other solid tumor types.

Evaluating Tumor Evolution via Genomic Profiling of Individual Tumor Spheroids in a Malignant Ascites

Abstract

Epithelial ovarian cancer (EOC) is a silent but mostly lethal gynecologic malignancy. Most patients present with malignant ascites and peritoneal seeding at diagnosis. In the present study, we used a laser-aided isolation technique to investigate the clonal relationship between the primary tumor and tumor spheroids found in the malignant ascites of an EOC patient. Somatic alteration profiles of ovarian cancer-related genes were determined for eight spatially separated samples from primary ovarian tumor tissues and ten tumor spheroids from the malignant ascites using next-generation sequencing. We observed high levels of intra-tumor heterogeneity (ITH) in copy number alterations (CNAs) and single-nucleotide variants (SNVs) in the primary tumor and the tumor spheroids. As a result, we discovered that tumor cells in the primary tissues and the ascites were genetically different lineages. We categorized the CNAs and SNVs into clonal and subclonal alterations according to their distribution among the samples. Also, we identified focal amplifications and deletions in the analyzed samples. For SNVs, a total of 171 somatic mutations were observed, among which 66 were clonal mutations present in both the primary tumor and the ascites, and 61 and 44 of the SNVs were subclonal mutations present in only the primary tumor or the ascites, respectively. Based on the somatic alteration profiles, we constructed phylogenetic trees and inferred the evolutionary history of tumor cells in the patient. The phylogenetic trees constructed using the CNAs and SNVs showed that two branches of the tumor cells diverged early from an ancestral tumor clone during an early metastasis step in the peritoneal cavity. Our data support the monophyletic spread of tumor spheroids in malignant ascites.

Conclusion

In this study, we performed genome-wide sequence analysis of the primary tumor and the associated tumor spheroids in the malignant ascites of an EOC patient. We analyzed genetic heterogeneity in the primary tumor and tumor spheroids through multi-region sequencing and the laser-aided cell isolation technique11. From the sequencing data, we discovered clonal or subclonal somatic CNAs and SNVs, based on which we constructed phylogenetic trees and inferred the evolutionary history of tumor cells in the patient. As a result, we found that the tumor cells in the malignant ascites were an independent lineage from the primary tumor. The phylogenetic analysis showed that the lineage branched before the evolution of the cancer cells at the primary tissues, which suggests that analyzing malignant ascites might be used to detect ovarian cancer or metastasis in the early stage. In summary, the genetic plasticity and similarity between a primary tumor and associated tumor spheroids are still not clear, and yet, the nature of the similarity may have profound implications for both tumor progression and therapeutic outcomes in ovarian cancer. Therefore, future prospective studies profiling the genomic information of primary ovarian tumors, distant metastatic tumors, and tumor spheroids to determine the direction of tumor evolution and metastasis of ovarian cancer are warranted.

Tumor evolution and intratumor heterogeneity of an epithelial ovarian cancer investigated using next-generation sequencing

Abstract

Background

The extent to which metastatic tumors further evolve by accumulating additional mutations is unclear and has yet to be addressed extensively using next-generation sequencing of high-grade serous ovarian cancer.

Methods

Eleven spatially separated tumor samples from the primary tumor and associated metastatic sites and two normal samples were obtained from a Stage IIIC ovarian cancer patient during cytoreductive surgery prior to chemotherapy. Whole exome sequencing and copy number analysis were performed. Omental exomes were sequenced with a high depth of coverage to thoroughly explore the variants in metastatic lesions. Somatic mutations were further validated by ultra-deep targeted sequencing to sort out false positives and false negatives. Based on the somatic mutations and copy number variation profiles, a phylogenetic tree was generated to explore the evolutionary relationship among tumor samples.

Results

Only 6% of the somatic mutations were present in every sample of a given case with TP53 as the only known mutant gene consistently present in all samples. Two non-spatial clusters of primary tumors (cluster P1 and P2), and a cluster of metastatic regions (cluster M) were identified. The patterns of mutations indicate that cluster P1 and P2 diverged in the early phase of tumorigenesis, and that metastatic cluster M originated from the common ancestral clone of cluster P1 with few somatic mutations and copy number variations.

Conclusions

Although a high level of intratumor heterogeneity was evident in high-grade serous ovarian cancer, our results suggest that transcoelomic metastasis arises with little accumulation of somatic mutations and copy number alterations in this patient.

Discussion

We have shown the range of genomic diversity at nucleotide, copy number and gene expression scales present in six patients with HGSC. These samples were resected prior to treatment, revealing intratumoural diversity in the tumours’ natural evolutionary state. Our results were consistent with recent reports 39 that considerable genomic diversity is measurable in HGSCs at the level of copy number. However, in some cases (cases 3 and 5) we observed large-scale copy number differences in spatially separated samples that were not evident at the level of mutational profiling, indicative of different mutational mechanisms operating independently in different parts of a tumour. Together, copy number and mutational profiling identified well-characterized mutations in genes such as PIK3CACTNNB1 and PDGFRB, as well as homozygous deletions in NF1, that were not present in all samples of the same case. With the exception of TP53, our data suggest that well-known, actionable driver mutations may only be partially represented if only a single sample is considered per patient. Our results establish that embedded within the context of extreme intra- and intertumour heterogeneity, TP53 mutation remains the most stable genomic feature in HGSC. However, as efforts towards precision medicine informed by mutational landscapes of tumours reach maturity, it will be imprudent to ignore the degree to which regional genomic variation is present in primary samples prior to any treatment-related selective pressures. Moreover, studies of evolution in cancer through analysis of serial biopsies (eg through pre- and post-treatment samples, or comparisons of primary and metastatic samples), will need to account for evidence of divergent profiles simply due to regional sampling bias.

Mutational profiling revealed for the first time that mutations beyond TP53 are present in FT lesions. In case 4, we used the full spectrum of mutations to establish that evolutionary trajectories of two histologically and karyotypically distinct tumours within a single patient arose from a common aetiology, with 26.2% of all mutations (including TP53) conserved in the tubal lesion. Our results indicate how histologically distinct cell populations in the same patient can be linked by common aetiology in the FT. In contrast to HGSC, endometrioid tumours are thought to develop from atypical ovarian endometriosis, with the FT merely acting as a conduit for endometrial epithelium to spread to the ovary (reviewed in 40). The possibility of the FT playing a causative role in rare cases of endometrioid carcinoma, or mixed endometrioid–serous carcinoma, has not yet been explored. Mutations in case 5 revealed the FT lesion to be a metastatic implant more closely related to a lymph node metastasis than to the ovarian samples. Thus, future investigations into aetiological underpinnings of HGSC will likely benefit from mutational comparison to extra-tubal sites to definitively establish the evolutionary origin of FT lesions.

We noted an extreme case of unexpected genomic conservation. In case 6, samples were obtained at primary surgery and after 42 months, with 21 cycles of multi-agent chemotherapy in between. The two samples exhibited near-identical genomic landscapes. Coupled with the long survivorship of this patient, this observation represents an intriguing anecdote. Larger studies will be needed to establish if stable clonal population structure is a feature of long survivorship.

Finally, as proof of concept, examination of plasma ctDNA suggested that representative mutations in the ancestral clone and mutations present in subclones could be detected in ctDNA, although sensitivity to detect mutations varied from patient to patient. This implies that clinical sensitivity analyses will need to be undertaken to establish the generalizability of ctDNA analysis to clonally diverse tumours.

In summary, our study unveils the extensive genomic diversity in primary, untreated, high-grade serous cancers of the ovary prior to treatment-related selection pressures, and illuminates highly individualized evolutionary trajectories that will require detailed consideration if curative therapeutic strategies are to be achieved.

 

Updated 3/04/2023 articles on specific tumor type intratumoral heterogeneity

Complex Patterns of Genomic Heterogeneity Identified in 42 Tumor Samples and ctDNA of a Pulmonary Atypical Carcinoid Patient

Source: Tamsin J. Robb, Peter Tsai, Sandra Fitzgerald, Paula Shields, Pascalene S. Houseman, Rachna Patel, Vicky Fan, Ben Curran, Rexson Tse, Jacklyn Ting, Nicole Kramer, Braden J. Woodhouse, Esther Coats, Polona Le Quesne Stabej, Jane Reeve, Kate Parker, Ben Lawrence, Cherie Blenkiron, Cristin G. Print; Complex Patterns of Genomic Heterogeneity Identified in 42 Tumor Samples and ctDNA of a Pulmonary Atypical Carcinoid PatientCancer Research Communications 3 January 2023; 3 (1): 31–42. https://doi.org/10.1158/2767-9764.CRC-22-0101

Tumor evolution underlies many challenges facing precision oncology, and improving our understanding has the potential to improve clinical care. This study represents a rare opportunity to study tumor heterogeneity and evolution in a patient with an understudied cancer type. A patient with pulmonary atypical carcinoid, a neuroendocrine tumor, metastatic to 90 sites, requested and consented to donate tissues for research. 42 tumor samples collected at rapid autopsy from 14 anatomically distinct sites were analyzed through DNA whole-exome sequencing and RNA sequencing, and five analyzed through linked-read sequencing. Targeted DNA sequencing was completed on two clinical tissue biopsies and one blood plasma sample. Chromosomal alterations and gene variants accumulated over time, and specific chromosomal alterations preceded the single predicted gene driver variant (ARID1A). At the time of autopsy, all sites shared the gain of one copy of Chr 5, loss of one copy of Chr 6 and 21, chromothripsis of one copy of Chr 11, and 39 small variants. Two tumor clones (carrying additional variants) were detected at metastatic sites, and occasionally in different regions of the same organ (e.g., within the pancreas). Circulating tumor DNA (ctDNA) sequencing detected shared tumor variants in the blood plasma and captured marked genomic heterogeneity, including all metastatic clones but few private tumor variants. This study describes genomic tumor evolution and dissemination of a pulmonary atypical carcinoid donated by a single generous patient. It highlights the critical role of chromosomal alterations in tumor initiation and explores the potential of ctDNA analysis to represent genomically heterogeneous disease. Significance: DNA sequencing data from tumor samples and blood plasma from a single patient highlighted the critical early role of chromosomal alterations in atypical carcinoid tumor development. Common tumor variants were readily detected in the blood plasma, unlike emerging tumor variants, which has implications for using ctDNA to capture cancer evolution.

In this article Robb et. al analyzes the tumor evolution of variants and mutations over time in a single patient.  Tumor samples were obtained from liquid biopsy as ctDNA, which is unlike the previous study in renal cancer, where multiple fine needle aspirates needed to be conducted to determine intratumoral heterogeneity with respect to spatial distribution within a solid tumor.  In this current study the authors were able to study the evolution of tumor heterogeneity in a single patient.

Tumor evolution underlies many of the most pressing challenges facing clinical precision oncology today, and a better understanding of this process has the potential to improve clinical care. We present a unique example of tumor evolution in an uncommon tumor type not previously featured in such studies, a pulmonary atypical carcinoid [an intermediate grade neuroendocrine tumor (NET)].

NETs arise from hormone-producing cells of the neuroendocrine system located throughout the body, and are highly heterogeneous, both genetically and pathologically. While once considered rare tumors (1) and thought to be indolent in nature (2), we now recognize that NETs have an age-adjusted incidence of 6.2 cases per 100,000 in the country where this study was undertaken, New Zealand (3), similar to the incidence of ovarian and cervical cancers (4). Pulmonary atypical carcinoids are mitotically active well-differentiated NETs, with one in five presenting with distant metastatic disease at diagnosis (5). They feature relatively few DNA variants, with recurrent variants occurring in chromatin remodeling genes such as MEN1 and ARID1A [reported in 25% and 10% of studied atypical carcinoids, respectively (6)]. In general, NETs feature few small DNA variants and often instead have large-scale chromosomal changes; for example, around 25% of pancreatic NETs lose a suite of 10 chromosomes, and a further 40% feature the loss of chromosome (Chr) 11 (7). Few genomic studies have been completed on pulmonary atypical carcinoids; however, comparative genomic hybridization studies identified recurrent deletions in Chr 11q (harboring MEN1) in around 60% of cases (8–10).

Tumor development is an evolutionary process with similarities to Darwinian natural selection, where tumor cells may be under multiple simultaneous “selective pressures” (11), including immune attack (12) and drug treatment (13). There are multiple debated models for the generation, propagation, and selection of variants throughout tumor development, including linear, branching, punctuated, and neutral evolution (11, 14), each of which may occur in different tumors or at different timepoints within the same patient. Tumor evolution has been associated with key clinical challenges, including cancer metastasis (15), immune evasion (12, 16), and drug resistance (13, 17, 18).

Sequencing the cell-free DNA (cfDNA) in a patient’s blood plasma to identify ctDNA variants derived from tumor cells has been postulated to better represent the total disease burden (including potential tumor heterogeneity) than single tumor biopsies or resections (19, 20). However, we do not yet fully understand the detectability of ctDNA shed from different anatomic sites around a patient’s body—and early results suggest that some tumor sites may be more easily detected than others (21).

In this study, we completed multimodal genomic analysis on samples collected at autopsy from 41 metastatic tumor sites from a single patient with an uncommon pulmonary atypical carcinoid, alongside a blood plasma sample and clinical biopsies, to catalog driver genomic alterations and hypothesize their order of accumulation. We compared the genomic variants identified in the tumors with those detected in the patient’s blood plasma to consider how well the ctDNA analysis represented the genomic tumor heterogeneity, and in turn, infer metastatic sites that clinical ctDNA assays may poorly detect.  A potential limitation, though, as stated by authors

“studies that analyze many tumors from a single patient, while revealing detailed and valuable insights about that patient’s cancer, cannot necessarily be generalized to other patients. Furthermore, all samples except for two clinical biopsies were collected at a single timepoint (at autopsy), limiting the extent of computational tumor evolution inferences that could be made, and the small size of these biopsies limited the scope of genomic assays that could be applied

In their conclusions they allude to the branched evolution that was seen with the renal cancer study:

Models of Tumor Evolution and Metastatic Dissemination

We can hypothesize a putative genomic evolution process (Fig. 7) that is consistent with the genomic heterogeneity observed at autopsy and in the two clinical biopsies. The early lung tumor represented by the diagnostic biopsy featured gain of Chr 5, loss of Chr 21 as well as nine small variants, none of which are known or predicted cancer drivers. Genomic features of the tumors collected at autopsy are consistent with further mutation after the lung biopsy was undertaken, followed by an evolutionary event such as a selective sweep, that fixed into the genomes of subsequent tumors a loss of function variant in ARID1A as well as 29 small non-driver variants and additional chromosomal alterations including Chr 11 chromothripsis and Chr 6 loss. There are plausible selective advantages to the tumor of each, including Chr 6 LOH of the HLA genes responsible for presenting peptides to T cells, hypothetically reducing the immune system’s ability to recognize neoantigens presented by the tumor cells (47). The data are consistent with subsequent branching events, including the accumulation of a second somatic “hit” to EPS8L2 (where the first was somatic LOH on Chr 11), before two parallel lineages developed, metastatic group 1 and 2 (Fig. 7). In metastatic group 1, some variants of unknown significance deserve further study. For instance, truncation of tumor suppressor ETV6 [a common fusion gene partner in breast and thyroid cancer (48)], following the LOH of a region of Chr 12 may be functionally significant given this gene’s role in development (49). The missense SLIT1 variant was the only additional protein-affecting variant carried by tumors in metastatic group 2 and may play a role in angiogenesis and migration (50). Interestingly, the two metastatic groups in the pancreas appear to have arisen by two independent seeding events. However, in the literature there is not overwhelming evidence for consistent genetic drivers of metastatic progression (51, 52) and we have no evidence in our data to suggest the additional genomic variants accumulated by each of the two metastatic groups (e.g., in EPS8L2ETV6, and SLIT1) were necessary to drive their metastasis. Given a general propensity of cancers to metastasize to lung (53), it is possible that the presence of multiple metastatic groups in the lung may be the result of tertiary metastatic seeding events returning tumor cells that have evolved elsewhere to the site of the lung primary tumor.

 

4/10/2013

Genomic instability is considered a hallmark and necessary for generating the mutations which drive tumorigenesis. Multiple studies had suggested that there may be multiple driver mutations and a plethora of passenger mutations driving a single tumor.  This diversity of mutational spectrum is even noticed in cultured tumor cells (refer to earlier post Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell).  Certainly, intratumor heterogeneity has been a concern to clinicians in determining the proper personalized therapy for a given cancer patient, and has been debated if multiple biopsies of a tumor is required to acquire a more complete picture of a tumor’s mutations.  In the New England Journal of Medicine, lead author Dr. Marco Gerlinger in the laboratory of Dr. Charles Swanton of the Cancer Research UK London Research Institute, and colleagues reported the results of a study to determine if intratumoral differences exist in the mutational spectrum of primary and metastatic renal carcinomas, pre- and post-treatment with the mTOR (mammalian target of rapamycin) inhibitor, everolimus (Afinitor®)[1].

The authors compared exome sequencing of multiregion biopsies from four patients with metastatic renal-cell carcinoma who had been enrolled in the Personalized RNA Interference to Enhance the Delivery of Individualized Cytotoxic and Targeted Therapeutics clinical trial of everolimus (E-PREDICT) before and after cytoreductive surgery.

Biopsies taken:

  • Multiregion spatial biopsy of primary tumor (representing 9 regions of the tumor)
  • Chest-wall metastases
  • Perinephric metastases
  • Germline DNA as control

Multiple platforms were used to determine aberrations as follows:

  1. Illumina Genome Analyzer IIx and Hiseq: for sequencing and mutational analysis
  2. Illumina Omni 2.5: for SNP (single nucleotide polymorphism)-array-based allelic imbalance detection for chromosomal imbalance and ploidy analysis
  3. Affymetrix Gene 1.0 Array: for mRNA analysis

A phylogenetic reconstruction of all somatic mutations occurring in primary disease and associated metastases was  performed to determine the clonal evolution of the metastatic disease given the underlying heterogeneity of the tumor.  Basically the authors wanted to know if the mutational spectra of one metastasis could be found in biopsies taken from the underlying primary tumor or if the mutational landscape of metastases had drastically changed.

Results

Multiregion exon-capture sequencing of DNA from pretreatment biopsy samples of the primary tumor, chest wall metastases, and perinephrous metastasis revealed 128 mutations classified as follows:

  • 40 ubiquitous mutations
  • 59 mutations shared by several but not all regions
  • 29 mutations unique to specific regions
  • 31 mutations shared by most primary tumor regions
  • 28 mutations shared by most metastatic regions

The authors mapped these mutations out with respect to their location, in order to determine how the metastatic lesions evolved from the primary tumor, given the massive heterogeneity in the primary tumor.  Construction of this “phylogenetic tree” (see Merlo et. al[2]) showed that the disease evolves in a branched not linear pattern, with one branch of clones evolving into a metastatic disease while another branch of clones and mutations evolve into the primary disease.

One of the major themes of the study is shown by results that an average of 70 somatic mutations were found in a single biopsy (a little more than just half of all tumor mutations) yet only 34% of the mutations in multiregion biopsies were detected in all tumor regions.

This indicated to the authors that “a single biopsy was not representative of the mutational landscape of the entire bulk tumor”. In addition, microarray studies concluded that gene-expression signatures from a single biopsy would not be able to predict outcome.

Everolimus therapy did not change the mutational landscape.  Interestingly, allelic composition and ploidy analyses revealed an extensive intratumor heterogeneity, with ploidy heterogeneity in two of four tumors and 26 of 30 tumor samples containing divergent allelic-imbalances.  This strengthens the notion that multiple clones with diverse genomic instability exist in various regions of the tumor.

 The intratumor heterogeneity reveals a convergent tumor evolution with associated heterogeneity in target function

Genes commonly mutated in clear cell carcinoma[3, 4] (and therefore considered the prevalent driver mutations for renal cancer) include:

Only VHL mutations were found in all regions of a given tumor, however there were three distinct SETD2 mutations (frameshift, splice site, missense) which were located in different regions of the tumor.

SETD2 trimethylates histones at various lysine residues, such as lysine residue 36 (H3K36).  The trimethylation of H3K36 is found on many actively transcribed genes.  Immunohistochemistry showed trimethylated H3K36 was reduced in cancer cells but positive in most stromal cells and in SETD2 wild-type clear-cell carcinomas.

Interestingly most regions of the primary tumor, except one, contained a kinase-domain activating mutation in mTOR.  Immunohistochemistry analysis of downstream target genes of mTOR revealed that mTOR activity was enhanced in regions containing this mutation.  Therefore the intratumoral heterogeneity corresponded to therapeutic activity, leading to the impression that a single biopsy may result in inappropriate targeted therapy.   Additional downstream biomarkers of activity confirmed both the intratumoral heterogeneity of mutational spectrum as well as an intratumoral heterogeneity of therapeutic-target function.

The authors conclude that “intratumor heterogeneity can lead to underestimation of the tumor genomics landscape from single tumor biopsies and may present major challenges to personalized-medicine and biomarker development”.

In an informal interview with Dr. Swanton, he had stressed the importance of performing these multi-region biopsies and the complications that intratumoral heterogeneity would present for personalized medicine, biomarker development, and chemotherapy resistance.

Q: Your data clearly demonstrates that multiple biopsies must be done to get a more complete picture of the tumor’s mutational landscape.  In your study, what percentage of the tumor would be represented by the biopsies you had performed?

Dr. Swanton: Realistically this is a very difficult question to answer, the more biopsies we sequence, the more we find, in the near term it may be very difficult to ever formally address this in large metastatic tumours

Q:  You have very nice data which suggest that genetic intratumor heterogeneity complicates the tumor biomarker field? do you feel then that quests for prognostic biomarkers may be impossible to attain?

Dr. Swanton: Not necessarily although heterogeneity is likely to complicate matters

Identifying clonally dominant lesions may provide better drug targets

Predicting resistance events may be difficult given the potential impact of tumour sampling bias and the concern that in some tumours a single biopsy may miss a relevant subclonal mutation that may result in resistance

Q:  Were you able to establish the degree of genomic instability among the various biopsies?

Dr. Swanton:  Yes, we did this by allelic imbalance analysis and found that the metastases were more genomically unstable than the primary region from which the metastasis derived

Q: I was actually amazed that there was a heterogeneity of mTOR mutations and SETD2 after everolimus therapy?   Is it possible these clones obtained a growth advantage?

Dr. Swanton: We think so yes, otherwise we wouldn’t identify recurrent mutations in these “driver genes”

Dr. Swanton will present his results at the 2013 AACR meeting in Washington D.C. (http://www.aacr.org/home/scientists/meetings–workshops/aacr-annual-meeting-2013.aspx)

The overall points of the article are as follows:

  • Multiple biopsies of primary tumor and metastases are required to determine the full mutational landscape of a patients 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

A great video of Dr. Swanton discussing his research can be viewed here

VIEW VIDEO

Everolimus: an inhibitor of mTOR

The following information was taken from the New Medicine Oncology Database (http://www.nmok.net)

Developer

Designation

Description

Approved/Filed Indications

Novartis PharmaCurrent as of: August 30, 2012 Generic Name: Everolimus
Brand Name: Afinitor
Other Designation: RAD001, RAD001C
RAD001, an ester of the macrocytic immunosuppressive agent sirolimus (rapamycin), is an inhibitor of mammalian target of rapamycin (mTOR) kinase.Administration Route: intravenous (IV) • PO • solid organ transplant
• renal cell carcinoma (RCC), metastatic after failure of treatment with sunitinib, sorafenib, or sunitinib plus sorafenib
• renal cell carcinoma, advanced, refractory to treatment with vascular endothelial growth factor (VEGF)-targeted therapy
• treatment of progressive neuroendocrine tumors (NET) of pancreatic origin (PNET) in patients with inoperable, locally advanced or metastatic disease

Marker Designation
Alias
Gene Location

Marker Description

Indications

5’-AMP-activated Protein Kinase (AMPK)AMPK beta 1 (beta1 non-catalytic subunit) • HAMPKb (beta1 non-catalytic subunit) • MGC17785 (beta1 non-catalytic subunit) • AMPK2 (alpha1 catalytic subunit) • PRKAA (alpha1 catalytic subunit) • AMPK alpha 1 (alpha1 catalytic subunit) • AMPKa1 ( AMPK is a member of a metabolite-sensing protein kinase family found in all eukaryotes. It functions as a cellular fuel sensor and its activation strongly suppresses cell proliferation in non-malignant cells and cancer cells. AMPK regulates the cell cycle by upregulating the p53-p21 axis and modulating the TSC2-mTOR (mammalian target of rapamycin) pathway. The AMPK signaling network contains a number of tumor suppressor genes including LKB1, p53, TSC1 and TSC2, and modulates growth factor signaling involving proto-oncogenes including PI3K, Akt and ERK. AMPK activation is therefore therapeutic target for cancer (Motoshima H, etal, J Physiol, 1 Jul 2006; 574(Pt 1): 63–71).AMPK is a protein serine/threonine kinase consisting of a heterotrimeric complex of a catalytic alpha subunit and regulatory ß and gamma subunits. AMPK is activated by increased AMP/ATP ratio, under conditions such as glucose deprivation, hypoxia, ischemia and heat shock. It is also activated by several hormones and cytokines. AMPK inhibits ATP-consuming cellular events, protein synthesis, de novo fatty acid synthesis, and generation of mevalonate and the downstream products in the cholesterol synthesis pathway (Motoshima H, etal, J Physiol, 1 Jul 2006; 574(Pt 1): 63–71). – ovarian cancer
– brain cancer
– liver cancer
– leukemia
– colon cancer
CREB regulated transcription coactivator 2 (CRTC2)TOR complex 2 (TORC2, mTORC2) • RP11-422P24.6 • transducer of regulated cAMP response element-binding protein (CREB)2 • transducer of CREB protein 2 • TOR1Location: 1q21.3 The mammalian target of rapamycin (mTOR) exists in two complexes, TORC1 and TORC2, which are differentially sensitive to rapamycin. cAMP response element-binding protein (CREB) regulated transcription coactivator 2 (CRTC2) or TORC2 is a multimeric kinase composed of mTOR, mLST8, mSin1, and rictor. The complex is insensitive to acute rapamycin exposure and functions in controlling cell growth and actin cytoskeletal assembly.TORC2 controls gene silencing, telomere length maintenance, and survival under DNA-damaging conditions. It is primaily located in the cytoplasm but also shuttles into the nucleus (Schonbrun M, etal, Mol Cell Biol, Aug 2009;29(16):4584-94). – brain cancer
Hypoxia inducible factor 1 alpha (HIF1A)HIF1-alpha (HIF-1 alpha) • HIF-1A • PASD8 • MOP1 • bHLHe78Location: 14q21-q24 The alpha subunit of the hypoxia inducible factor 1 (HIF-1alpha) is a 826 amino acid antigen consisting of a basic helix-loop-helix (bHLH)-PAS domain at its N-terminus. HIF-1alpha is rapidly degraded by the proteasome under normal conditions, but is stabilized by hypoxia resulting in the transactivation of several proangiogenic genes. HIF-1alpha is responsible for inducing production of new blood vessels as needed when tumors outgrow existing blood supplies. HIF-1alpha serves as a transcriptional factor that regulates gene expression involved in response to hypoxia and promotes angiogenesis.HIF-1alpha is a proangiogenic transcription factor induced primarily by tumor hypoxia that is critically involved in tumor progression, metastasis and overall tumor survival. HIF-1alpha functions as a survival factor that is required for tumorigenesis in many types of malignancies, and is expressed in a majority of metastases and late-stage tumors. HIF-1alpha is overexpressed in brain, breast, colon, endometrial, head and neck, lung, ovarian, and pancreatic cancer, and is associated with increased microvessel density and/or VEGF expression – prostate cancer
– bladder cancer
– nasopharyngeal cancer
– head and neck cancer
– kidney cancer
– pancreatic cancer
– endometrial cancer
– breast cancer
Mammalian target of rapamycin (mTOR)FK506 binding protein 12-rapamycin associated protein 1 • RAFT1 • FK506 binding protein 12-rapamycin associated protein 2 • FRAP • FRAP1 • FRAP2 • RAPT1 • FKBP-rapamycin associated protein • FKBP12-rapamycin complex-associated protein 1 • rapamycin target protein • TOR • FLJ44809 • MTORC1 • MTORC2 • RPTOR • RAPTOR • KIAA1303 • mammalian target of rapamycin complex 1Location: 1p36.22 The mammalian target of rapamycin (mTOR) is a large serine/threonine protein (Mr 300,000) having heat repeats, and protein-protein interaction domains at its amino terminus, and a protein kinase domain at its carboxy terminus. mTOR is a member of the phosphoinositide 3-kinase (PI3K)-related kinase (PIKK) family and a central modulator of cell growth. It regulates cell growth, proliferation and survival by impacting on protein synthesis and transcription. mTOR is present in two multi-protein complexes, a rapamycin-sensitive complex, TOR complex 1 (TORC1), defined by the presence of Raptor and a rapamycin insensitive complex, TOR complex 2 (TORC2), with Rictor, Protor and Sin1. Rapamycin selectively inhibits mTORC1 by binding indirectly to the mTOR/Raptor complex via FKBP12, resulting in inhibition of p70S6kinase but not the mTORC2 substrate AKTSer473. Selective inhibition of p70S6K attenuates negative feedback loops to IRS1 and TORC2 resulting in an increase in pAKT which may limit the activity of rapamycin.In a hypoxic environment the increase in mass of solid tumors is dependent on the recruitment of mitogens and nutrients. As a function of nutrient levels, particularly essential amino acids, mTOR acts as a checkpoint for ribosome biogenesis and cell growth. Ribosome biogenesis has long been recognized in the clinics as a predictor of cancer progression; increase in size and number of nucleoli is known to be associated with the most aggressive tumors and a poor prognosis. In bacteria, ribosome biogenesis is independently regulated by amino acids and energy charge. The mTOR pathway is controlled by intracellular ATP levels, independent of amino acids, and mTOR itself is an ATP sensor (Kozma SC, etal, AACR02, Abs. 5628). – breast cancer
– pancreatic cancer
– multiple myeloma
– liver cancer
– brain cancer
– prostate cancer
– kidney cancer
– lymphoma
Signal transducer and activator of transcription 3 (STAT3)Stat-3 • acute-phase response factor (APRF) • FLJ20882 • HIESLocation: 17q21 Signal transducer and activator of transcription 3 (STAT3) is a member of the STAT protein family. STAT3, plays a critical role in hematopoiesis. STAT3 is located in the cytoplasm and translocated to the nucleus after tyrosine phosphorylation. In response to cytokines and growth and other activation factors, STAT family members are phosphorylated by the receptor associated kinases and then form homo- or heterodimers, which translocate to the cell nucleus where they act as transcription activators. – multiple myeloma
– hematologic malignancy
– lymphoma
Sonic hedgehog homolog (SHH)Shh • HHG1 • HHG-1 • holoprosencephaly 3 (HPE3) • HLP3 • SMMCILocation: 7q36 Sonic hedgehog, a secreted hedgehog ligand, is a human homolog of the Drosophila segment polarity gene hedgehog, cloned by investigators at Harvard University (Marigo V, etal, Genomics, 1 Jul 1995;28 (1):44-51).The mammalian sonic hedgehog (Shh) pathway controls proliferation of granule cell precursors in the cerebellum and is essential for normal embryonic development. Shh signaling is disrupted in a variety of malignancies. – pancreatic cancer
– CNS cancer

References:

1.         Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P et al: Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. The New England journal of medicine 2012, 366(10):883-892.

2.         Merlo LM, Pepper JW, Reid BJ, Maley CC: Cancer as an evolutionary and ecological process. Nature reviews Cancer 2006, 6(12):924-935.

3.         Varela I, Tarpey P, Raine K, Huang D, Ong CK, Stephens P, Davies H, Jones D, Lin ML, Teague J et al: Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature 2011, 469(7331):539-542.

4.         Dalgliesh GL, Furge K, Greenman C, Chen L, Bignell G, Butler A, Davies H, Edkins S, Hardy C, Latimer C et al: Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes. Nature 2010, 463(7279):360-363.

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AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In VivoGenomics of bronchial epithelial dysplasia

Genomics in Medicine- Tomorrow’s Promise

Prostate Cancer: Androgen-driven “Pathomechanism” in Early-onset Forms of the Disease

CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease – Part IIC

CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics and Computational Genomics – Part IIB

Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell

Directions for Genomics in Personalized Medicine

LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2

Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1

Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @ http://pharmaceuticalintelligence.com

In Focus: Targeting of Cancer Stem Cells

Modulating Stem Cells with Unread Genome: microRNAs

What can we expect of tumor therapeutic response?

 

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