Posts Tagged ‘driver mutations’

Lymph Node Metastases

Larry H. Bernstein, MD, FCAP, Curator


Investigation of the Lack of Angiogenesis in the Formation of Lymph Node Metastases

Journal of the National Cancer Institute

Han-Sin Jeong; Dennis Jones; Shan Liao; Daniel A. Wattson; Cheryl H. Cui; Dan G. Duda; Christopher G. Willett; Rakesh K. Jain; Timothy P. Padera

J Natl Cancer Inst. 2015; 107(9)

Background: To date, antiangiogenic therapy has failed to improve overall survival in cancer patients when used in the adjuvant setting (local-regional disease with no detectable systemic metastasis). The presence of lymph node metastases worsens prognosis, however their reliance on angiogenesis for growth has not been reported.

Methods: Here, we introduce a novel chronic lymph node window (CLNW) model to facilitate new discoveries in the growth and spread of lymph node metastases. We use the CLNW in multiple models of spontaneous lymphatic metastases in mice to study the vasculature of metastatic lymph nodes (n = 9–12). We further test our results in patient samples (n = 20 colon cancer patients; n = 20 head and neck cancer patients). Finally, we test the ability of antiangiogenic therapy to inhibit metastatic growth in the CLNW. All statistical tests were two-sided.

Results: Using the CLNW, we reveal the surprising lack of sprouting angiogenesis during metastatic growth, despite the presence of hypoxia in some lesions. Treatment with two different antiangiogenic therapies showed no effect on the growth or vascular density of lymph node metastases (day 10: untreated mean = 1.2%, 95% confidence interval [CI] = 0.7% to 1.7%; control mean = 0.7%, 95% CI = 0.1% to 1.3%; DC101 mean = 0.4%, 95% CI = 0.0% to 3.3%; sunitinib mean = 0.5%, 95% CI = 0.0% to 1.0%, analysis of variance P = .34). We confirmed these findings in clinical specimens, including the lack of reduction in blood vessel density in lymph node metastases in patients treated with bevacizumab (no bevacizumab group mean = 257 vessels/mm2, 95% CI = 149 to 365 vessels/mm2; bevacizumab group mean = 327 vessels/mm2, 95% CI = 140 to 514 vessels/mm2P = .78).

Conclusion: We provide preclinical and clinical evidence that sprouting angiogenesis does not occur during the growth of lymph node metastases, and thus reveals a new mechanism of treatment resistance to antiangiogenic therapy in adjuvant settings. The targets of clinically approved angiogenesis inhibitors are not active during early cancer progression in the lymph node, suggesting that inhibitors of sprouting angiogenesis as a class will not be effective in treating lymph node metastases.


Although antiangiogenic therapy is standard of care for several advanced (metastatic) cancers, all phase III clinical trials of antiangiogenic therapy to date have failed in the adjuvant setting.[1–4] The presence of lymph node metastases—the most common form of cancer dissemination—dictates treatment decisions,[5,6] however their reliance on angiogenesis for growth has not been reported. Furthermore, observations from preclinical and clinical studies suggest that lymph node metastases and primary tumors can respond differently to the same therapeutic regimen.[7–9] The clinical relevance of lymph node metastases has been the subject of debate for many years. Some argue that the presence of lymph node metastasis only demonstrates the ability of the cancer to metastasize and that disease in the lymph node is inconsequential.[10,11] The strong predictive power of lymph node metastases has led others to hypothesize that cancer cells in the lymph node can exit and spread to distant metastatic sites.[12,13] These advocates argue disease in lymph nodes needs to be treated in order to prevent distant metastasis and ultimately eradicate disease from the patient.[14,15] Likely the answer lies in between, depending where on the spectrum of progression to distant metastasis the cancer is diagnosed.[16]These issues highlight our fundamental lack of understanding of the biology of how metastatic cancer cells grow in a lymph node and affect the overall prognosis for the patient, limiting our ability to discover effective adjuvant therapy to treat lymph node metastases.

We and others have previously shown that antiangiogenic therapy did not stop the seeding or growth of lymph node metastases,[9,17,18] but no mechanism of failure has been determined. Nonsprouting angiogenesis mechanisms to sustain tumor growth, such as vessel co-option and intussusception, have been implicated in the growth of lung, liver, and brain metastases[19] and are thought to play a role in resistance to antiangiogenic therapy.[20] Based on these findings, we hypothesized that early growth of lymph node metastases is not dependent on sprouting angiogenesis.

Although reports show reduced vascular density in lymph node metastases compared with corresponding primary tumors and surrounding normal lymph node,[17,21,22] these data do not describe the degree of angiogenesis or whether the vessels are functional. Here, we introduce a novel model to longitudinally image the formation and growth of metastatic tumors in lymph nodes and reveal the surprising lack of sprouting angiogenesis, despite the presence of hypoxia in some lesions. Treatment with two different therapies designed to target sprouting angiogenesis showed no effect on the growth or vascular density of lymph node metastases in our models. These data are corroborated in clinical specimens and further add to mechanisms for the failure of antiangiogenic treatments in adjuvant settings.[1–4,20]


Intravital Multiphoton Microscopy

Intravital multiphoton microscopy was carried out as described previously on a custom-built multiphoton microscope.[25] Details of the imaging equipment, imaging protocols, and image analysis can be found in the Supplementary Methods (available online).


Longitudinal Imaging of the Formation of Spontaneous Lymph Node Metastases Using a Novel Chronic Lymph Node Window

Holding back our understanding of the biology of lymph node metastasis is our inability to longitudinally monitor spontaneous lymph node metastases. Inspired by pioneering intravital microscopy of the lymph node,[30–35] we developed a chronic lymph node window (CLNW)—a modification of the mammary fat pad chamber[23,24]—to create a CLNW that allows intravital imaging for up to 14 days with minimal morphological, cellular or biochemical changes in the inguinal lymph node (Figure 1, A and B; Supplementary Figure 1, available online).

Using multiphoton microscopy in the CLNW, we were able to serially image various stages of the growth of spontaneous metastasis in the lymph node from murine SCCVII squamous cell carcinoma[36,37]transduced with green fluorescence protein (SCCVII-GFP) (Figure 1C). Initially, cancer cells remain in or near the subcapsular sinus as individual cells (Figure 1C). Later, small aggregates of a few cancer cells form near the subcapsular sinus, which then grow into metastatic lesions that invade deeper into the lymph node (Figure 1C). This sequence was also observed in syngeneic MCa-P0008 breast cancer and B16F10 melanoma cells lines (Supplementary Figure 2, available online).

Recent genomic studies suggest that metastatic cells within lymph nodes consist of multiple clones.[38,39]To investigate this concept, we transduced SCCVII and SCCVII-GFP cells with a red fluorescence protein (DsRed), producing three different colors of cells (red, green, and red+green) that were mixed in equal proportions to form primary tumors. Single cells of multiple colors disseminated from the multicolor primary tumor and grew in the subcapsular sinus (Figure 1D). The metastatic lesions that subsequently formed contained all three colors with great spatial heterogeneity (Figure 1D), suggesting that lymph node metastases form from multiple cells. These findings were reproduced when using an equal mix of 4T1-DsRed and 4T1-GFP mammary carcinoma cells implanted in the mammary fat pad. In contrast, more than 80% of detected lung metastases from these 4T1 tumors were single color (Figure 1E).

The Role of the Existing Lymph Node Vascular Supply in Supporting the Growth of Lymph Node Metastases

Next, we directly measured for the first time whether angiogenesis is occurring in lymph node metastases by using intravital multiphoton microscopy to make longitudinal measurements in our CLNW. In early stages, metastatic cells resided in the lymph node sinus, away from blood vessels (Figure 2A). These metastatic tumor cells eventually invaded the lymph node cortex, growing closer to functional lymph node blood vessels and presumably utilizing the nutrient supply of these pre-existing vessels (Figure 2A). We found that the tumor cells started to access host lymph node blood vessels when they invaded approximately 50 to 100 μm into the cortex (Figure 2, B and C). Although the tumor invaded deeper into the node (day 6 mean depth = 43 μm, 95% CI = 24 to 61 μm; day 12 mean depth = 131 μm, 95% CI = 71 to 191 μm, P = .01), blood vessels did not invade toward the surface of the lymph node (day 6 mean depth = 52 μm, 95% CI = 49 to 55 μm; day 20 mean depth = 58 μm, 95% CI = 41 to 75 μm,P = .38), as would be expected for tumor-induced sprouting angiogenesis. These data provide the first direct evidence of the lack of sprouting angiogenesis during the growth of metastatic lesions in the lymph node.

Figure 2.

Intravital imaging of lymph node metastases and the native lymph node vasculature. A) Representative time course of images from a single metastatic lymph node, showing cancer cells (SCCVII, green) and blood vessels (TRITC-dextran, red) at three different depths in tissue. The image was created using multiphoton microscopy, and second harmonic generation was used to highlight fibrillar collagen (blue) in the lymph node capsule. The images are created from maximum intensity projections of 25 μm of tissue from inside the lymph node. In day 40 images, the red signal is background signal from the accumulation of TRITC-dextran as a result of the five intravenous injections over the course of the metastatic growth. Yellow arrows identify individual cancer cells. Yellow circles identify areas in which many cancer cells are found in the subcapsular sinus. White arrows identify blood vessels in the metastatic lesion. Purple, green and light blue arrows identify features in the lymph node vasculature that can be used to identify the same region in the mouse over the multiday experiment. White line marks edge of lymph node. Scale bars = 100 μm. B) A vertical image reconstruction showing the tumor cells (SCCVII, green) initially growing above the blood vessels (red). C) Measurements of the maximum depth of tumor cell invasion (SCCVII) and the minimum depth of blood vessels. Data are presented as mean ± 95% confidence interval.

Immunofluorescent staining for CD31 (Figure 3A) showed that the vessel density in lymph nodes with micrometastases from SCCVII tumors (Figure 3B) and macrometastases (lesions greater than 500 microns in one dimension) from 4T1 tumors (Figures 3E; Supplementary Figure 3A, available online) were not increased compared with those of control (from naïve mice with no tumor implantation) and contralateral nodes. The vessel density inside metastatic lesions was lower than the surrounding lymph node tissue (vessel density: SCCVII: metastatic lesion = 1.0%, 95% CI = 0.0% to 2.0%; nontumor area = 7.0%, 95% CI = 1.0% to 13.0%, P = .04; 4T1: metastatic lesion = 4.0%, 95% CI = 1.0% to 7.0%; nontumor area = 10.0%, 95% CI = 5.0% to 15.0%, P = .04) (Figure 3, C and F). To indicate sprouting angiogenesis, Ki67—a marker of cell proliferation—showed no difference in endothelial cell proliferation in micrometastatic lymph nodes (SCCVII) (Figure 3D; Supplementary Figure 4, available online) and a reduction in endothelial cell proliferation in macrometastatic lymph nodes (4T1) (Figure 3G; Supplementary Figure 3B, available online) in comparison with control and contralateral nodes. Vessel density in the metastatic lesions was not related to lesion size (Supplementary Figure 3C, available online). These data further indicate that sprouting angiogenesis is not induced in the lymph node at this stage of cancer progression.

Figure 3.

Immunohistochemical analysis of lymph node blood vessels and metastases. A) Representative sections of control (from non–tumor bearing mice), contralateral, and tumor-draining lymph nodes with micrometastases (SCCVII, green). Vessels were stained with CD31 (red) and nuclei with DAPI (blue).Scale bars = 300 μm. B) Quantification of CD31+ area per lymph node area in control, contralateral, and micrometastatic lymph nodes. C) In micrometastatic lymph nodes, quantification of CD31+ area per tissue area comparing tumor areas with nontumor areas. D) Costaining for CD105 and Ki67 measured blood vessel proliferation in micrometastatic lymph nodes. E) Using a different tumor model (4T1) that formed macrometastasis in the lymph node (greater than 500 μm in one direction), we measured CD31+ area in micrometastatic or macrometastatic lymph nodes, compared with control or contralateral nodes. F) The vascular area of macrometastatic lesions was measured in tumor areas and nontumor lymph node tissue. G) Costaining for CD31 and Ki67 measured blood vessel proliferation in macrometastatic lymph nodes. Data are presented as mean ± 95% confidence interval. Statistical significance was tested by one-way analysis of variance with Tukey’s Honestly Significant Difference post hoc test (B, D, E, G) or two-tailed paired Student’s t test (C, F).

In contrast, LYVE-1 staining for lymphatic vessels showed an increase in lymphatic vascular area (vessel density: SCCVII: control = 5.0%, 95% CI = 3.0% to 7.0%; contralateral = 8.0%, 95% CI = 6.0% to 10.0%; metastatic = 10.0%, 95% CI = 6.0% to 14.0%; control vs metastatic P = .03; 4T1: control = 5.0%, 95% CI = 2.0% to 8.0%; contralateral = 9.0%, 95% CI = 6.0% to 12.0%; nonmetastatic tumor draining = 22.0%, 95% CI = 18.0% to 26.0%; metastatic = 4.0%, 95% CI = 1.0% to 7.0%; control vs nonmetastatic tumor draining P < .001) and proliferating lymphatic endothelial cells in draining lymph nodes from SCCVII and 4T1 tumors (Supplementary Figures 5 and 6, available online), consistent with previous reports.[40–43] Interestingly, the lymphatic vascular area was greater in the contralateral and nonmetastatic tumor-draining lymph nodes of 4T1-bearing mice compared with lymph nodes with macrometastatic lesions (P < .001) (Supplementary Figure 6, available online), suggesting that the presence of cancer cells causes the lymphatic vasculature to regress. When compared with lymph nodes from tumor-naïve animals, contralateral lymph nodes show greater lymphatic vascular density (SCCVII: P = .04; 4T1: P < .001), suggesting that contralateral lymph nodes are also affected by the presence of the primary tumor, as others have reported.[44]

Although lesions growing in the subcapsular sinus of the lymph node showed markers for hypoxia (Figure 4, A–D), sprouting angiogenesis was not induced in these lesions and they remained avascular. Metastatic lesions that invaded the lymph node parenchyma where functional nodal blood vessels reside had only focally heterogeneous areas positive for hypoxia markers (Figure 4, A, C, and E). These data suggest that growing metastatic lesions can utilize the existing lymph node vasculature in order to meet their metabolic demand. Whether this demand or hypoxia drives cancer cell invasion of the lymph node remains unknown.

Figure 4.

Hypoxia in lymph node metastases. A) Representative images of pimonidazole staining for hypoxia (green) and perfused lectin staining for functional blood vessels (red) in lymph node metastases from 4T1 mammary carcinoma (cytokeratin, blue). The top panels show a lesion in the subcapsular sinus that is hypoxic and has no perfused blood vessels in the lesion. The bottom panels show a lesion in the parenchyma of the lymph node with perfused blood vessels and no hypoxia. Dashed line shows edge of the lymph node. Scale bars = 100 μm. B) Higher magnification of pimonidazole staining in metastatic lymph node showing colocalization of cytokeratin and pimonidazole. Contralateral lymph node is non–tumor bearing. Dashed line shows edge of the lymph node. Scale bars = 50 μm. C) Quantification of pimonidazole and perfused vessel staining in metastatic lesions in the subcapsular sinus and lymph node parenchyma. Data are presented as mean ± 95% confidence interval. Statistical significance was tested by two-tailed unpaired Student’s t test. D and E) Staining for CAIX, a marker of the cellular response to hypoxia, and CD31-positive blood vessels shows similar results to pimonidazole staining. Dashed line shows the outline of the metastatic lesions. Scale bars = 636 μm.

Hypoxia generally induces the production of vascular endothelial growth factor (VEGF). However, VEGF levels in control, contralateral, and metastatic lymph nodes were not different (4T1: control = 0.3 pg VEGF/mg protein, 95% CI = 0.2 to 0.4 pg VEGF/mg protein; contralateral = 0.4 pg VEGF/mg protein, 95% CI = 0.3 to 0.5 pg VEGF/mg protein; metastatic = 0.5 pg VEGF/mg protein, 95% CI = 0.2 to 0.8 pg VEGF/mg protein; Figure 5A; SCCVII: control = 0.4 pg VEGF/mg protein, 95% CI = 0.3 to 0.5 pg VEGF/mg protein; contralateral = 0.4 pg VEGF/mg protein, 95% CI = 0.3 to 0.5 pg VEGF/mg protein; metastatic = 0.4 pg VEGF/mg protein, 95% CI = 0.3 to 0.5 pg VEGF/mg protein; Figure 5B; and E0771: control = 0.3 pg VEGF/mg protein, 95% CI = 0.2 to 0.4 pg VEGF/mg protein; contralateral = 0.4 pg VEGF/mg protein, 95% CI = 0.3 to 0.5 pg VEGF/mg protein; metastatic = 0.4 pg VEGF/mg protein, 95% CI = 0.3 to 0.5 pg VEGF/mg protein; Figure 5C; all P values > .05 for each ANOVA containing these three lymph nodes types). Furthermore, levels of VEGF-C and VEGF-D were lower in metastatic and nonmetastatic tumor draining lymph nodes when compared with naïve lymph nodes (Supplementary Figure 6, C and D, available online). Next, we screened for transcriptional changes in sprouting angiogenesis-related genes in lymph nodes with metastasis when compared with naïve lymph nodes. No pro-angiogenesis related genes were upregulated in metastatic lymph nodes, but thrombospondin-1 (Thbs-1) and TIMP-1—both of which are antiangiogenic—were upregulated (Figure 5D). We confirmed no change in Vegf levels (control = 0.24 VEGF/GAPDH, 95% CI = 0.06 to 0.42 VEGF/GAPDH; metastatic = 0.16 VEGF/GAPDH, 95% CI = 0.04 to 0.28 VEGF/GAPDH, P = .37) and the elevation in Thbs-1 in lymph node metastasis by quantitative polymerase chain reaction (qPCR) (control = 0.10 THBS-1/GAPDH, 95% CI = 0.05 to 0.15 THBS-1/GAPDH; metastatic = 0.38 THBS-1/GAPDH, 95% CI = 0.23 to 0.53 THBS-1/GAPDH; P = .001) (Figure 5E). Thrombospondin-1 (TSP-1) was specifically located surrounding the blood vessels of control, contralateral, and metastatic lymph nodes (Figure 5F), further defining the nonangiogenic phenotype associated with these vessels. Taken together, these data describe an environment lacking prosprouting angiogenesis stimuli and abundant in antiangiogenesis molecules, suggesting metastatic lesions in the lymph node do not induce nor rely upon sprouting angiogenesis during their early growth.

Figure 5.

Molecular signature of quiescent lymph node vasculature. A-C) Levels of vascular endothelial growth factor (VEGF) protein were measured in metastatic lymph nodes containing 4T1 (A), SCCVII (B), or E0771 (C) and compared with control and contralateral lymph nodes. D) Quantitative polymerase chain reaction (qPCR) transcriptional array for angiogenesis-related genes compared the transcriptional profile of a diaeresis lymph node to a tumor-bearing lymph node. Differentially transcribed genes were defined as having more than a four-fold change and a P value under .01 when comparing metastatic lymph nodes to diaeresis lymph nodes. E) Confirmation of the qPCR transcriptional array for the Vegf and Thbs1 genes. *P < .05. F) Dual immunofluorescence staining for CD31 (red) and TSP-1 (green) showed distinctive TSP-1 staining surrounding the blood vessels in diaeresis, contralateral, and metastatic lymph nodes. Scale bars = 100μm. Data are presented as mean ± 95% confidence interval. Statistical significance was tested by one-way analysis of variance with Tukey’s Honestly Significant Difference post hoc test (A, B, and C) and two-tailed unpaired Student’s t test (E).

Blood Vessel Density in Metastatic Lymph Nodes From Colon Cancer and Head and Neck Cancer Patients

To confirm these findings in clinical specimens in a cancer where angiogenesis inhibitors have shown efficacy, we stained lymph nodes from 20 colon cancer patients with lymphatic metastasis for CD31 (Figure 6A; Supplementary Figure 7A, online). These patients did not have metastases on initial staging and went directly for surgical resection with no prior cancer-directed treatments (eg, chemotherapy, radiation therapy). We found that blood vessel densities in metastatic lymph nodes and large metastatic lesions where lymph node tissue was completely replaced with tumor cells were on average lower than those of tumor-negative lymph nodes (nonmetastatic- = 220 blood vessels/mm2, 95% CI = 172 to 268 blood vessels/mm2; metastatic = 135 blood vessels/mm2, 95% CI = 113 to 157 blood vessels/mm2; lymph node replaced by cancer = 104 blood vessels/mm2, 95% CI = 75 to 133 blood vessels/mm2; comparisons of either group of tumor-bearing to nonmetastatic lymph nodes: P < .001) (Figure 6, B and C). Furthermore, the vessel density inside metastatic lesions was statistically significantly lower than in the remaining lymph node tissue (metastatic lesion = 148 blood vessels/mm2, 95% CI = 124 to 172 blood vessels/mm2; nontumor area = 115 blood vessels/mm2, 95% CI = 95 to 135 blood vessels/mm2P = .03) (Figure 6, D and E). Accordingly, TSP-1 staining was also found to associate with lymph node blood vessels and to surround the gland-like structures formed by the cancer cells (Supplementary Figure 7B, available online), further suggesting that these vessels were not undergoing sprouting angiogenesis. Finally, the density of CD31-positive vessels was not dependent on the lesion size in the section, showing that vessel densities of macrometastases (clinically classified as lesions larger than 2mm in one direction[45]]) are the same as in micrometastases (Figure 6F). Blood vessel density and TSP-1 staining in specimens from head and neck cancer patients were similar to those from colon cancer patients (Supplementary Figure 7, C–G, available online). Taken together, these data from two different patient populations support the concept that the growth of metastatic lesions in the lymph nodes is not dependent upon sprouting angiogenesis.

Figure 6.

Vascular density in metastatic lymph nodes from colon cancer patients. A) Representative images of nonmetastatic (n = 19) and metastatic (n = 39) lymph nodes as well as lymph node tumors in which no normal lymph node tissue remained (n = 9). The sections were stained with CD31 (brown) to identify blood vessels. Scale bars = 200 μm. Images of whole lymph node sections can be found in Supplementary Figure 7 (available online). B) The number of vessels per area as determined by CD31 staining was measured in metastatic lymph nodes and in lymph node tumors in which no normal lymph node tissue remained and compared with nonmetastatic lymph nodes. C) The fraction of lymph node area composed of CD31-positive vessels was similarly measured in metastatic lymph nodes and in lymph node tumors in which no normal lymph node tissue remained and compared with nonmetastatic lymph nodes. *P value was determined by Tukey’s Honestly Significant Difference post hoc test of analysis of variance model. D and E) Within a metastatic lymph node, vascular density (D) and vessel area fraction (E) were measured in the tumor and the nontumor area. * P value was determined by paired Student’s t test.F) Vessel density was not dependent on the lesion size. Data are presented as mean ± 95% confidence interval throughout figure.

Growth of Lymph Node Metastases With Antiangiogenic Treatment

To directly measure the response of lymph node metastases to antiangiogenic therapy in the CLNW, we began treatment when micrometastases were between 100 and 125 μm in diameter (5–10×10–3 mm3)—the stage when we found blood vessels surrounding lymph node metastases—with either a monoclonal VEGF receptor (VEGFR)-2–blocking antibody (DC101, ImClone Systems) or the pan-VEGFR small-molecule tyrosine kinase inhibitor sunitinib. We chose agents with differential mechanisms of VEGF pathway inhibition—monoclonal antibody vs tyrosine kinase inhibitor (TKI)—to understand whether our findings were agent specific. Measuring lymph node blood vessels using the CLNW and longitudinal multiphoton microscopy, the growth of lymph node metastases (Figure 7, A–C) and functional blood vessel volume density remained at similar levels during treatment with either DC101 or sunitinib when compared with untreated controls (vessel density: day 10: untreated = 1.2%, 95% CI = 0.7% to 1.7%; control = 0.7%, 95% CI = 0.1% to 1.3%; DC101 = 0.4%, 95% CI = 0.0% to 3.3%; sunitinib = 0.5%, 95% CI = 0.0% to 1.0%; ANOVA P = .34) (Figure 7D). These direct measurements, supported by previous endpoint studies,[9,17] suggest that inhibitors of sprouting angiogenesis as a class of drugs will not be effective in inhibiting the early phase of lymph node metastasis. In contrast, sunitinib—a pan-VEGF receptor TKI—reduced the elevated lymphatic vessel density found in early metastatic lymph nodes compared with PBS control (Supplementary Figure 8, available online).

Figure 7.

Antiangiogenic therapy in the early growth of lymph node metastases. A) Representative intravital multiphoton microscopy images of spontaneous lymph node metastases treated with vehicle control, sunitinib, or the blocking monoclonal anti–VEGFR-2 antibody DC101. Tumor cells are shown in green and blood vessels in redScale bars = 200 μm. B) Primary tumors were of equal size at the time treatment began, when the lymph node micrometastases were 5–10×10–3 mm3C) The growth rate of the metastatic tumor in the lymph node was measured during antiangiogenesis therapy. D) The vessel density in metastatic lesions in the lymph node was measured during antiangiogenesis therapy. Biological replicates: untreated n = 15 (C), 12 (D), control (IgG = 2, PBS = 4) n = 6, sunitinib = 6, DC101 = 5. Data are presented as mean ± 95% confidence interval. Statistical significance was tested by one-way analysis of variance with Tukey’s Honestly Significant Difference post hoc test (B and C) and two-tailed unpaired Student’s t test (D and E).

Blood Vessel Density of Lymph Node Metastasis From Patients Treated With Bevacizumab

Finally, we identified rectal cancer patients that received neoadjuvant chemoradiation and bevacizumab and a comparator cohort of rectal cancer patients who received only neoadjuvant chemoradiation, as previously described.[46,47] Despite downstaging of the primary tumor after neoadjuvant therapy, lymph node metastases were often found at the time of surgery and pathological evaluation. Comparing lymph node metastases from 10 patients in each group, we found no difference in the vessel density in lymph node metastases (no bevacizumab group mean = 257 vessels/mm2, 95% CI = 149 to 365 vessels/mm2; bevacizumab group mean = 327 vessels/mm2, 95% CI = 140 to 514 vessels/mm2P = .78) (Figure 8, A and B). The vascular density in the tumor lesions specifically was also not different between the groups (no bevacizumab group mean = 307 blood vessels/mm2, 95% CI = 186 to 428 vessels/mm2; bevacizumab group mean = 318 blood vessels/mm2, 95% CI = 118 to 518 vessels/mm2P = .60) (Figure 8, C and D). Metastatic lymph nodes showed lower vascular density than nonmetastatic nodes after neoadjuvant therapy (Figure 8, A and B), independent of whether bevacizumab was used. Finally, lymphatic vessel density was not different in metastatic and nonmetastatic lymph nodes when comparing patients who received bevacizumab to those who did not (Supplementary Figure 8, D and E, available online). These data provide the first clinical evidence for the lack of response of lymph node metastasis to antiangiogenic therapy.

Figure 8.

Vascular density in lymph node metastases in rectal cancer patients treated with bevacizumab. The number of CD31+ vessels per area (A) and the fraction of lymph node area composed of CD31+ vessels(B) were measured in nonmetastatic and metastatic lymph nodes in colorectal cancer (CRC) patients that received neoadjuvant chemoradiation (No Bev.) or neoadjuvant chemoradiation with bevacizumab (Bev.).P value was determined by two-tailed unpaired Student’s t test. C and D) Within the tumor area of metastatic lymph nodes, we measured vascular density (C) and vessel area fraction (D) in rectal cancer patients that received neoadjuvant chemoradiation (No Bev.) or neoadjuvant chemoradiation with bevacizumab (Bev.). P value was determined by two-tailed unpaired Student’s t test. Data are presented as mean ± 95% confidence interval.


The main concept driving antiangiogenic therapy has been the hypothesis that tumors depend on new blood vessel growth. A critical observation made by longitudinal intravital microscopy in the CLNW is that metastatic lesions did not induce sprouting angiogenesis as they grew, in spite of the presence of hypoxia. Lesions that invaded into the blood vessel–rich lymph node parenchyma showed reduced hypoxia, suggesting that cancer cells survive in the lymph node by utilizing the existing lymph node vascular supply. The lack of VEGF, VEGF-C, and VEGF-D, along with the presence of TSP-1 surrounding lymph node blood vessels, provides a mechanism behind the lack of sprouting angiogenesis observed in lymph node metastases. A limitation of the use of longitudinal intravital microscopy is the limited imaging depth of 300 μm by multiphoton microscopy in the CLNW. To balance this, we used histological techniques, which allow full lymph node depth to be characterized but are limited in their ability to monitor the kinetic changes occurring as metastatic lesions grow in the lymph node. Using these complimentary techniques allowed better characterization of the growth of lymphatic metastases.

Our data show lymph node lymphangiogenesis is an early event in the natural history of cancer progression, in agreement with previous studies.[40,41,43] However, decreased lymphatic vessel density was found in macrometastatic lymph nodes, suggesting that the presence of the cancer cells in the lymph node causes lymphatic vessel regression. Furthermore, bevacizumab did not statistically significantly affect the lymphatic vasculature in patients. These data suggest that late intervention with antiangiogenic or antilymphangiogenic therapies after lymphatic vessel regression has begun in patients will show no effect on lymph node lymphatic vessels.

In patients, the observation that large metastatic lesions do not exhibit increased vascular density relative to those with micrometastases further suggests that sprouting angiogenesis is not required to sustain the growth of lymph node metastases. A limitation of our data is that we estimated lesion size based on the two-dimensional area available in the histological sections, so we are likely underestimating the size of the lesion. An additional limitation of our study is that we cannot rule out the contribution from different modes of new blood vessel formation in lymph node metastasis such as vasculogenesis, intussusception, vessel co-option, vascular mimicry, and tumor cell differentiation into endothelial cells.[20] The mechanisms of these alternative processes are not clearly defined, although VEGF and endothelial proliferation have been shown to contribute to these processes.[48–51] Our preclinical and clinical data, however, show that inhibitors targeting primarily sprouting angiogenesis will not inhibit the growth of metastases in the lymph node.

Predicted by recent genomic data,[38,39,52] we provide direct evidence that lymph node metastasis forms from multiple cells that disseminate from the primary tumor and suggest a fundamental difference in their formation compared with hematogenous metastases. Cancer cells that invade lymphatic vessels travel to the draining lymph node where they enter in locations defined by afferent lymphatic vessels. As such, lymph node metastasis can be reinforced by the continual arrival of new cells as they gain a foothold in their new microenvironment, leading to the spatially heterogeneous lesions imaged here and the genetically heterogeneous lesions documented previously.[38,39,52,53] In contrast, cells that metastasize through the blood spread out to different locations in an organ by the branching vasculature, leading to a higher probability of individually homogenous lesions. One can thus speculate that targeting a single genetic trait, unless ubiquitous in the primary tumor, may not be effective in eradicating lymph node metastases and any subsequent spread to distant sites.[39]

Using multiple spontaneous metastasis models, we show the first direct evidence that sprouting angiogenesis is not required in lymph node lesions during early metastatic growth. The lack of sprouting angiogenesis in lymph node metastases suggests an additional explanation for the poor outcomes of antiangiogenic therapy in adjuvant settings. As the lymph node is able to metabolically support rapid cellular expansion during an active immune response, it seems the existing vasculature of the lymph node is also able to support the growth of a nascent metastasis. Thus, the mechanisms of angiogenesis and the targets of clinically approved drugs are not active during this early step in cancer progression, suggesting that inhibitors of sprouting angiogenesis as a class will not be effective in treating lymph node metastases. Our novel preclinical models provide opportunities to uncover strategies to better control and eradicate disease in lymph nodes in metastatic cancer patients.

Gene Mutation Signals Poor Prognosis for Pancreatic Tumors

College of American Pathologists (CAP) 2015 Meeting

Neil Osterweil

NASHVILLE, Tennessee — For patients with pancreatic neuroendocrine tumors, the presence of recently identified mutations in two key genes is a prognostic factor for poor outcome, researchers report.

“We found loss of nuclear expression in about 23% of the tumors that we studied, and this loss of expression was associated with worse tumors from the outset,” lead investigator Michelle Heayn, MD, a second-year pathology resident at the University of Pittsburgh Medical Center, told Medscape Medical News.

Pancreatic tumors with neuroendocrine histology frequently respond to chemotherapy and have a more favorable prognosis than the more common pancreatic adenocarcinomas. However, the mutations are associated with worse disease-free and disease-specific survival.

The results of the study were presented here at the College of American Pathologists 2015 Meeting.

The mutations — in the alpha-thalassemia mental retardation syndrome X-linked gene (ATRX) and the death-domain-associated protein gene (DAXX) — cause loss of expression of the proteins coded by ATRX and DAXX, Dr Heayn explained.

We found loss of nuclear expression in about 23% of the tumors that we studied.

To test whether these mutations had any prognostic significance, Dr Heayn and her colleagues used immunolabeling in surgically resected pancreatic neuroendocrine tumors from 303 patients. They then correlated the findings with patient demographics, pathologic features, disease-free survival, and disease-specific survival. Follow-up ranged from 1.6 to 18.8 years.

Of the 303 tumors, 69 (23%) had mutations in one or both genes. Tumors with a gene mutation had a larger mean diameter than tumors with intact gene expression (5.0 vs 2.4 cm), as well as a significantly higher histologic grade, more lymphovascular and perineural invasion, a more advanced T stage, greater lymph node involvement, more synchronous metastases, and more frequent disease recurrence (P < .01 for all comparisons).

In addition, the mutations were associated with shorter mean disease-free survival (5.6 vs 17.2 years;< .01) and shorter mean disease-specific survival (12.5 vs 17.7 years; P = .01).

On multivariate analysis that controlled for patient and tumor factors, the mutations were a significant predictor of shorter disease-free survival (P < .01), independent of tumor size, stage, histology, lymphovascular or perineural invasion, and lymph node status.

Dr Heayn and her colleagues are currently exploring whether there is an association between metastatic pancreatic cancer and these genetic mutations.

Metastatic Pancreatic Cancer

Patients with these mutations in their tumors should be followed more closely for recurrence or disease progression, Dr Heayn said. And in this subset of patients, there is the possibility of new targeted therapies.

These findings are very important, said Safia Salaria, MD, from the Vanderbilt University Medical Center in Nashville.

“There is so much heterogeneity in these tumors, and currently we are just using clinicopathologic features and the WHO-recommended Ki-67 labelling and white count,” she told Medscape Medical News.

“If we have something that can be an adjunct to that — immunohistochemistry to determine the loss of these genes — it’s definitely going to be something that will help us, especially in low-grade tumors,” she explained.

Staining for the expression of the genes could also help pathologists identify patients who are at higher risk for disease recurrence or metastasis but don’t have metastases at the time of primary resection, Dr Salaria said.

Microbiome May Predict Colon Cancer Tumor Mutational Status

Neil Osterweil

BALTIMORE — Analysis of the microbiome surrounding colon cancer tumors could be used as a noninvasive screening test that is more sensitive and specific than fecal occult blood testing, according to the results of a new study.

“This is something that could be critical in colon cancer, because each tumor may have a different mutational landscape with different genes mutated, and that might have an effect on the microbiome,” said Ran Blekhman, PhD, from the University of Minnesota in Minneapolis.

The results of the study were presented here at the American Society of Human Genetics 2015.

Dr Blekhman and his colleagues looked at the genetic differences between healthy colon cells and tumor cells from adults with colorectal cancer, and found that specific tumor mutations are associated with the presence of specific bacteria in the gut.

For example, in people with an APC gene mutation, there is a strong association between familial adenomatous polyposis, a hereditary cancer syndrome, and an abundance of Fusobacterium, said Dr Blekhman.

He pointed out that his lab is the first to analyze the correlation between specific tumor mutations and the composition of the tumor microbiome.

More Mutations, More Diversity

The investigators used whole-exome sequencing to assess the protein-coding regions of tumors and microbiome profiling to characterize the microbiota in tumor biopsy specimens and normal colon tissue samples from 44 adults with colon cancer.

They found that the more mutations, the more varied the bacterial species in the tumor microbiome.

And for certain genes, there was a correlation between somatic mutations and changes in the abundance of specific microbes.

Other evidence of the correlation between bacteria and tumor was seen at the pathway level.

Loss-of-function mutations were detected in tumor glucose transport pathways and were strongly correlated with higher levels of energy utilization in the microbiome, said Dr Blekhman. This suggests that the tumor and the bacteria in its neighborhood are competing for bodily resources.

The investigators created a risk index that evaluated the correlation between microbes and each of several known tumor driver mutations. The index was able to accurately predict the presence of a loss-of-function mutation in ZFN717, a gene encoding for a zinc finger nuclease, part of a family of enzymes involved in DNA repair.

These findings suggest that it is possible to genetically classify tumors from fecal samples alone. Theoretically, this means that manipulation of the tumor microenvironment could be used to prevent or treat colon cancer, Dr Blekhman explained.

This study addresses, in part, the problem of “hidden heritability,” said Chris Gunter, PhD, from Emory University School of Medicine in Atlanta.

“If you look at cancer-sequencing studies now, they identify something like 10 possible driver mutations. We have not yet managed to predict what all the drivers and passengers will be,” she told Medscape Medical News.

“If this type of work can help us narrow down the list, that should add to our understanding of how cancer develops,” she said.


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

Reporter: Stephen J. Williams, Ph.D.

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

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

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

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


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


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

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

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

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

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

As Dr. Swanton stated:

“we are underestimating the frequency of polyclonal evolution”

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

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

Targeting Tumor Heterogeneity

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

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

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


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


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


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

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

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

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

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

Mitochondrial Isocitrate Dehydrogenase and Variants

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

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.

lung cancer

(photo credit:

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:

Sequence files:


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:


  • 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:


  • 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.


  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 »

Cancer Mutations Across the Landscape

Curator: Larry H. Bernstein, MD, FCAP

This is an up-to-date article about the significance of mutations found in 12 major types of cancer.

Mutational landscape and significance across 12 major cancer types

Cyriac Kandoth1*, Michael D. McLellan1*, Fabio Vandin2, Kai Ye1,3, Beifang Niu1, Charles Lu1, et al.

1The Genome Institute, Washington University in St Louis, Missouri 63108, USA. 2Department of Computer Science, Brown University, Providence, Rhode Island 02912, USA. 3Department of Genetics, Washington University in St Louis, Missouri 63108, USA. 4Department of Medicine, Washington University in St Louis, Missouri 63108, USA. 5Siteman Cancer Center, Washington University in St Louis, Missouri 63108, USA. 6Department of Mathematics, Washington University in St Louis, Missouri 63108, USA.

NATURE 17 Oct 2013;  5 0 2

The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort. We illustrate

  1. the distributions of mutation frequencies,
  2. types and contexts across tumour types, and
  3. establish their links to tissues of origin,
  4. environmental/ carcinogen influences, and
  5. DNA repair defects.

Using the integrated data sets, we identified 127 significantly mutated genes from well-knownand emerging cellular processes in cancer.

  1. (for example, mitogen-activated protein kinase, phosphatidylinositol-3-OH kinase,Wnt/b-catenin and receptor tyrosine kinase signalling pathways, and cell cycle control)
  2. (for example, histone, histone modification, splicing, metabolism and proteolysis)

The average number of mutations in these significantly mutated genes varies across tumour types;

  1. most tumours have two to six, indicating that the number of driver mutations required during oncogenesis is relatively small.
  2. Mutations in transcriptional factors/regulators show tissue specificity, whereas
  3. histone modifiers are often mutated across several cancer types.

Clinical association analysis identifies genes having a significant effect on survival, and

  • investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis.

Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment


The advancement of DNA sequencing technologies now enables the processing of thousands of tumours of many types for systematic mutation discovery. This expansion of scope, coupled with appreciable progress in algorithms1–5, has led directly to characterization of signifi­cant functional mutations, genes and pathways6–18. Cancer encompasses more than 100 related diseases19, making it crucial to understand the commonalities and differences among various types and subtypes. TCGA was founded to address these needs, and its large data sets are providing unprecedented opportunities for systematic, integrated analysis.

We performed a systematic analysis of 3,281 tumours from 12 cancer types to investigate underlying mechanisms of cancer initiation and progression. We describe variable mutation frequencies and contexts and their associations with environmental factors and defects in DNA repair. We identify 127 significantlymutated genes (SMGs) from diverse signalling and enzymatic processes. The finding of a TP53-driven breast, head and neck, and ovarian cancer cluster with a dearth of other mutations in SMGs suggests common therapeutic strategies might be applied for these tumours. We determined interactions among muta­tions and correlated mutations in BAP1, FBXW7 and TP53 with det­rimental phenotypes across several cancer types. The subclonal structure and transcription status of underlying somatic mutations reveal the trajectory of tumour progression in patients with cancer.

Standardization of mutation data

Stringent filters (Methods) were applied to ensure high quality muta­tion calls for 12 cancer types: breast adenocarcinoma (BRCA), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), uterine corpus endometrial carcinoma (UCEC), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), colon and rectal carcinoma (COAD, READ),bladder urothelial carcinoma (BLCA), kidney renal clear cell carcinoma (KIRC), ovarian serous carcinoma (OV) and acute myeloid leukaemia (LAML; conventionally called AML) (Supplementary Table 1). A total of 617,354 somatic mutations, consisting of

  • 398,750 missense,
  • 145,488 silent,
  • 36,443 nonsense,
  • 9,778 splice site,
  • 7,693 non-coding RNA,
  • 523 non-stop/readthrough,
  • 15,141 frameshift insertions/deletions (indels) and
  • 3,538 inframe indels,

were included for downstream analyses (Supplementary Table 2).

Distinct mutation frequencies and sequence context

Figure 1a shows that AML has the lowest median mutation frequency and LUSC the highest (0.28 and 8.15 mutations per megabase (Mb), respectively). Besides AML, all types average over 1 mutation per Mb, substantially higher than in pediatric tumours20. Clustering21 illus­trates that

  • mutation frequencies for KIRC, BRCA, OV and AML are normally distributed within a single cluster, whereas
  • other types have several clusters (for example, 5 and 6 clusters in UCEC and COAD/ READ, respectively) (Fig. 1a and Supplementary Table 3a, b).

In UCEC, the largest patient cluster has a frequency of approximately 1.5 muta­tions per Mb, and

  • the cluster with the highest frequency is more than 150 times greater.

Multiple clusters suggest that factors other than age contribute to development in these tumours14,16. Indeed,

  • there is a significant correlation between high mutation frequency and DNA repair pathway genes (for example, PRKDC, TP53 and MSH6) (Sup­plementary Table 3c). Notably,
  • PRKDC mutations are associated with high frequency in BLCA, COAD/READ, LUAD and UCEC, whereas
  • TP53 mutations are related with higher frequencies in AML, BLCA, BRCA, HNSC, LUAD, LUSC and UCEC (all P < 0.05).

Mutations in POLQ and POLE associate with high frequencies in multiple cancer types; POLE association in UCEC is consistent with previous observations14.

Comparison of spectra across the 12 types (Fig. 1b and Supplemen­tary Table 3d) reveals that LUSC and LUAD contain increased C>A transversions, a signature of cigarette smoke exposure10. Sequence context analysis across 12 types revealed

  • the largest difference being in C>T transitions and C>G transversions (Fig. 1c).

The frequency of thymine 1-bp (base pair) upstream of C>G transversions is mark­edly higher in BLCA, BRCA and HNSC than in other cancer types (Extended Data Fig. 1). GBM, AML, COAD/READ and UCEC have similar contexts in that

  • the proportions of guanine 1 base downstream of C>T transitions are between
    • 59% and 67%, substantially higher than the approximately 40% in other cancer types.

Higher frequencies of transition mutations at CpG in gastrointestinal tumours, including colorectal, were previously reported22. We found three additional cancer types (GBM, AML and UCEC) clustered in the C>T mutation at CpG, consistent with previous findings of

  • aberrant DNA methylation in endometrial cancer23 and glioblastoma24.

BLCA has a unique signature for C>T transitions compared to the other types (enriched for TC) (Extended Data Fig. 1).

Significantly mutated genes

Genes under positive selection, either in individual or multiple tumour types, tend to display higher mutation frequencies above background. Our statistical analysis3, guided by expression data and curation (Methods), identified 127 such genes (SMGs; Supplementary Table 4). These SMGs are involved in a wide range of cellular processes, broadly classified into 20 categories (Fig. 2), including

  • transcription factors/regulators, histone modifiers, genome integrity, receptor tyrosine kinase signal­ling, cell cycle, mitogen-activated protein kinases (MAPK) signalling, phosphatidylinositol-3-OH kinase (PI(3)K) signalling, Wnt/ -catenin signalling, histones, ubiquitin-mediatedproteolysis, and splicing (Fig. 2).

The identification of MAPK, PI(3)K and Wnt/ -catenin signaling path­ways is consistent with classical cancer studies. Notably, newer categories (for example, splicing, transcription regulators, metabolism, proteolysis and histones) emerge as exciting guides for the development of new therapeutic targets. Genes categorized as histone modifiers (Z = 0.57), PI(3)K signalling (Z = 1.03), and genome integrity (Z = 0.66) all relate to more than one cancer type, whereas

  • transcription factor/regulator (Z = 0.40), TGF- signalling (Z = 0.66), and Wnt/ -catenin signalling (Z = 0.55) genes tend to associate with single types (Methods).

Notably, 3,053 out of 3,281 total samples (93%) across the Pan-Cancer collection had at least one non-synonymous mutation in at least one SMG. The average number of point mutations and small indels in these genes varies across tumour types, with the highest (,6 mutations per tumour) in UCEC, LUAD and LUSC, and the lowest (,2 mutations per tumour) in AML, BRCA, KIRC and OV. This suggests that the numbers of both cancer-related genes (only 127 identified in this study) and cooperating driver mutations required during oncogenesis are small (most cases only had 2–6) (Fig. 3), although large-scale structural rearrangements were not included in this analysis.

Common mutations

The most frequently mutated gene in the Pan-Cancer cohort is TP53 (42% of samples). Its mutations predominate in serous ovarian (95%) and serous endometrial carcinomas (89%) (Fig. 2). TP53 mutations are also associated with basal subtype breast tumours. PIK3CA is the second most commonly mutated gene, occurring frequently (>10%) in most cancer types except OV, KIRC, LUAD and AML. PIK3CA mutations frequented UCEC (52%) and BRCA (33.6%), being speci­fically enriched in luminal subtype tumours. Tumours lacking PIK3CA mutations often had mutations in PIK3R1, with the highest occur­rences in UCEC (31%) and GBM (11%) (Fig. 2).

Many cancer types carried mutations in chromatin re-modelling genes. In particular, histone-lysine N-methyltransferase genes (MLL2 (also known as KMT2D), MLL3 (KMT2C) and MLL4 (KMT2B)) clus­ter in bladder, lung and endometrial cancers, whereas the lysine (K)-specific demethylase KDM5C is prevalently mutated in KIRC (7%). Mutations in ARID1A are frequent in BLCA, UCEC, LUAD and LUSC, whereas mutations in ARID5B predominate in UCEC (10%) (Fig. 2).

Fig. 1.  Distribution of mutation frequencies across 12 cancer types.

Fig. 1.  | Distribution of mutation frequencies across 12 cancer types.

Dashed grey and solid white lines denote average across cancer types and median for each type, respectively. b, Mutation spectrum of six transition (Ti) and transversion (Tv) categories for each cancer type. c, Hierarchically clustered mutation context (defined by the proportion of A, T, C and G nucleotides within ±2bp of variant site) for six mutation categories. Cancer types correspond to colours in a. Colour denotes degree of correlation: yellow (r = 0.75) and red (r = 1).

Fig. 2.  The 127 SMGs from 20 cellular processes in cancer identified in and Pan-Cancer are shown, with the highest percentage in each gene among 12 (not shown)

Fig. 3.  Distribution of mutations in 127 SMGs across Pan-Cancer cohort.

Fig. 3. | Distribution of mutations in 127 SMGs across Pan-Cancer cohort.

Box plot displays median numbers of non-synonymous mutations, with outliers shown as dots. In total, 3,210 tumours were used for this analysis (hypermutators excluded).

Figure 4 | Unsupervised clustering based on mutation status of SMGs. Tumours having no mutation or more than 500 mutations were excluded. A mutation status matrix was constructed for 2,611 tumours. Major clusters of mutations detected in UCEC, COAD, GBM, AML, KIRC, OV and BRCA were highlighted.
Complete gene list shown in Extended Data Fig. 3.  (not shown)

Fig. 5. Driver initiation and progression mutations and tumour clonal mutation is in the subclone

Figure 5 | Driver initiation and progression mutations and tumour clonal mutation is in the subclone

Survival Analysis

We examined which genes correlate with survival using the Cox proportional hazards model, first analysing individual cancer types using age and gender as covariates; an average of 2 genes (range: 0–4) with mutation frequency 2% were significant (P<_0.05) in each type (Supplementary Table 10a and Extended Data Fig. 6). KDM6A and ARID1A mutations correlate with better survival in BLCA (P = 0.03, hazard ratio (HR) = 0.36, 95% confidence interval (CI): 0.14–0.92) and UCEC (P = 0.03, HR = 0.11, 95% CI: 0.01–0.84), respectively, but mutations in SETBP1, recently identified with worse prognosis in atypical chronic myeloid leukaemia (aCML)31, have a significant detrimental effect in HNSC (P = 0.006, HR = 3.21, 95% CI: 1.39–7.44). BAP1 strongly correlates with poor survival (P = 0.00079, HR = 2.17, 95% CI: 1.38–3.41) in KIRC. Conversely, BRCA2 muta­tions (P = 0.02, HR = 0.31, 95% CI: 0.12–0.85) associate with better survival in ovarian cancer, consistent with previous reports32,33; BRCA1 mutations showed positive correlation with better survival, but did not reach significance here.

We extended our survival analysis across cancer types, restricting our attention to the subset of 97 SMGs whose mutations appeared in 2% of patients having survival data in 2 tumour types. Taking type, age and gender as covariates, we found 7 significant genes: BAP1DNMT3AHGFKDM5CFBXW7BRCA2 and TP53 (Extended Data Table 1).  In particular, BAP1 was highly significant (0.00013, HR = 2.20, 95% CI: 1.47–3.29, more than 53 mutated tumours out of 888 total), with mutations associating with detrimental outcome in four tumour types and notable associations in KIRC (P = 0.00079), consistent with a recent report28, and in UCEC(P = 0.066). Mutations in several other genes are detrimental, including DNMT3A (HR = 1.59), previously identified with poor prognosis in AML34, and KDM5C (HR = 1.63), FBXW7 (HR = 1.57) and TP53 (HR = 1.19). TP53 has significant associations with poor outcome in KIRC (P = 0.012), AML (P = 0.0007) and HNSC (P = 0.00007). Conversely, BRCA2 (P = 0.05, HR = 0.62, 95% CI: 0.38 to 0.99) correlates with survival benefit in six types, including OV and UCEC (Supplementary Table 10a, b). IDH1 mutations are associated with improved prognosis across the Pan-Cancer set (HR = 0.67, P = 0.16) and also in GBM (HR = 0.42, P = 0.09) (Supplementary Table 10a, b), consistent with previous work.35

 Driver mutations and tumour clonal architecture

To understand the temporal order of somatic events, we analysed the variant allele fraction (VAF) distribution of mutations in SMGs across AML, BRCA and UCEC (Fig. 5a and Supplementary Table 11a) and other tumour types (Extended Data Fig. 7). To minimize the effect of copy number alterations, we focused on mutations in copy neutral segments. Mutations in TP53 have higher VAFs on average in all three cancer types, suggesting early appearance during tumorigenesis.

It is worth noting that copy neutral loss of heterozygosity is commonly found in classical tumour suppressors such as TP53, BRCA1, BRCA2 and PTEN, leading to increased VAFs in these genes. In AML, DNMT3A (permutation test P = 0), RUNX1 (P = 0.0003) and SMC3 (P = 0.05) have significantly higher VAFs than average among SMGs (Fig. 5a and Supplementary Table 11b). In breast cancer, AKT1, CBFB, MAP2K4, ARID1A, FOXA1 and PIK3CA have relatively high average VAFs. For endometrial cancer, multiple SMGs (for example, PIK3CA, PIK3R1, PTEN, FOXA2 and ARID1A) have similar median VAFs. Conversely, KRAS and/or NRAS mutations tend to have lower VAFs in all three tumour types (Fig. 5a), suggesting NRAS (for example, P = 0 in AML) and KRAS (for example, P = 0.02 in BRCA) have a progression role in a subset of AML, BRCA and UCEC tumours. For all three cancer types, we clearly observed a shift towards higher expression VAFs in SMGs versus non-SMGs, most apparent in BRCA and UCEC (Extended Data Fig. 8a and Methods).

Previous analysis using whole-genome sequencing (WGS) detected subclones in approximately 50% of AML cases15,36,37; however, ana­lysis is difficult using AML exome owing to its relatively few coding mutations. Using 50 AML WGS cases, sciClone ( genome/sciclone) detected DNMT3A mutations in the founding clone for 100% (8 out of 8) of cases and NRAS mutations in the subclone for 75% (3 out of 4) of cases (Extended Data Fig. 8b). Among 304 and 160 of BRCA and UCEC tumours, respectively, with enough coding muta­tions for clustering, 35% BRCA and 44% UCEC tumours contained subclones. Our analysis provides the lower bound for tumour hetero­geneity, because only coding mutations were used for clustering. In BRCA, 95% (62 out of 65) of cases contained PIK3CA mutations in the founding clone, whereas 33% (3 out of 9) of cases had MLL3 muta­tions in the subclone. Similar patterns were found in UCEC tumours, with 96% (65 out of 68) and 95% (62 out of 65) of tumours containing PIK3CA and PTEN mutations, respectively, in the founding clone, and 9% (2 out of22) ofKRAS and 14% (1 out of 7) ofNRAS mutations in the subclone (Extended Data Fig. 8b and Supplementary Table 12).

Mutation con­text (-2 to +2 bp) was calculated for each somatic variant in each mutation category, and hierarchical clustering was then performed using the pairwise mutation context correlation across all cancer types. The mutational significance in cancer (MuSiC)3 package was used to identify significant genes for both indi­vidual tumour types and the Pan-Cancer collective. An R function ‘hclust’ was used for complete-linkage hierarchical clustering across mutations and samples, and Dendrix30 was used to identify sets of approximately mutual exclusive muta­tions. Cross-cancer survival analysis was based on the Cox proportional hazards model, as implemented in the R package ‘survival’ ( packages/survival/), and the sciClone algorithm ( generated mutation clusters using point mutations from copy number neutral segments. A complete description of the materials and methods used to generate this data set and its results is provided in the Methods.

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

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

Author: Larry H. Bernstein, MD, FCAP, Triplex Medical Science

Part I: The Initiation and Growth of Molecular Biology and Genomics – Part I From Molecular Biology to Translational Medicine: How Far Have We Come, and Where Does It Lead Us?

Part II: CRACKING THE CODE OF HUMAN LIFE is divided into a three part series.

Part IIA. “CRACKING THE CODE OF HUMAN LIFE: Milestones along the Way” reviews the Human Genome Project and the decade beyond.

Part IIB. “CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics” lays the manifold multivariate systems analytical tools that has moved the science forward to a groung that ensures clinical application.

Part IIC. “CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease “ will extend the discussion to advances in the management of patients as well as providing a roadmap for pharmaceutical drug targeting.

To be followed by:
Part III will conclude with Ubiquitin, it’s role in Signaling and Regulatory Control.

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

This final paper of Part II concludes a thorough review of the scientific events leading to the discovery of the human genome, the purification and identification of the components of the chromosome and the DNA structure and role in regulation of embryogenesis, and potential targets for cancer.

The first two articles, Part IIA, Part IIB,  go into some depth to elucidate the problems and breakthoughs encountered in the Human Genome Project, and the construction of a 3-D model necessary to explain interactions at a distance.

Part IIC, the final article, is entirely concerned with clinical application of this treasure trove of knowledge to resolving diseases of epigenetic nature in the young and the old, chronic inflammatory diseases, autoimmune diseases, infectious disease, gastrointestinal disorders, neurological and neurodegenerative diseases, and cancer.

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

1. Gene Links to Heart Disease


Recently, large studies have identified some of the genetic basis for important common diseases such as heart disease and diabetes, but most of the genetic contribution to them remains undiscovered. Now researchers at the University of Massachusetts Amherst led by biostatistician Andrea Foulkes have applied sophisticated statistical tools to existing large databases to reveal substantial new information about genes that cause such conditions as high cholesterol linked to heart disease.

Foulkes says, “This new approach to data analysis provides opportunities for developing new treatments.” It also advances approaches

  • to identifying people at greatest risk for heart disease. Another important point is that our method is straightforward to use with freely
  • available computer software and can be applied broadly to advance genetic knowledge of many diseases.

The new analytical approach she developed with cardiologist Dr. Muredach Reilly at the University of Pennsylvania and others is called “Mixed modeling of Meta-Analysis P-values” or MixMAP. Because it makes use of existing public databases, the powerful new method

  • represents a low-cost tool for investigators.
  • MixMAP draws on a principled statistical modeling framework and the vast array of summary data now available from genetic association
  • studies to formally test at a new, locus-level, association.

While that traditional statistical method looks for one unusual “needle in a haystack” as a possible disease signal, Foulkes and colleagues’

  • new method uses knowledge of DNA regions in the genome that are likely to
  • contain several genetic signals for disease variation clumped together in one region.
  • Thus, it is able to detect groups of unusual variants rather than just single SNPs, offering a way to “call out” gene
  • regions that have a consistent signal above normal variation. News/Identify Genes Linked to Heart Disease/

2. Apolipoprotein(a) Genetic Sequence Variants

The LPA gene codes for apolipoprotein(a), which, when linked with low-density lipoprotein particles, forms lipoprotein(a) [Lp(a)] —

  • a well-studied molecule associated with coronary artery disease (CAD). The Lp(a) molecule has both atherogenic and thrombogenic effects in vitro , but the extent to which these translate to differences in how atherothrombotic disease presents is unknown.

LPA contains many single-nucleotide polymorphisms, and 2 have been identified by previous groups as being strongly associated with

  • levels of Lp(a) and, as a consequence, strongly associated with CAD.

However, because atherosclerosis is thought to be a systemic disease, it is unclear to what extent Lp(a) leads to atherosclerosis in other arterial beds (eg, carotid, abdominal aorta, and lower extremity),

  • as well as to other thrombotic disorders (eg, ischemic/cardioembolic stroke and venous thromboembolism).

Such distinctions are important, because therapies that might lower Lp(a) could potentially reduce forms of atherosclerosis beyond the coronary tree.

To answer this question, Helgadottir and colleagues compiled clinical and genetic data on the LPA gene from thousands of previous

  • participants in genetic research studies from across the world. They did not have access to Lp(a) levels, but by knowing the genotypes for
  • 2 LPA variants, they inferred the levels of Lp(a) on the basis of prior associations between these variants and Lp(a) levels. [1]

Their studies included not only individuals of white European descent but also a significant proportion of black persons, in order to

  • widen the generalizability of their results.

Their main findings are that LPA variants (and, by proxy, Lp(a) levels) are associated with

  • CAD,
  • peripheral arterial disease,
  • abdominal aortic aneurysm,
  • number of CAD vessels,
  • age at onset of CAD diagnosis, and
  • large-artery atherosclerosis-type stroke.

They did not find an association with

  • cardioembolic or small-vessel disease-type stroke;
  • intracranial aneurysm;
  • venous thrombosis;
  • carotid intima thickness; or,
  • in a small subset of individuals, myocardial infarction.

Apolipoprotein(a) Genetic Sequence Variants Associated With Systemic Atherosclerosis and Coronary Atherosclerotic Burden but Not With Venous Thromboembolism. Helgadottir A, Gretarsdottir S, Thorleifsson G, et al.    J Am Coll Cardiol. 2012;60:722-729

English: Structure of the LPA protein. Based o...

English: Structure of the LPA protein. Based on PyMOL rendering of PDB 1i71. (Photo credit: Wikipedia)

Micrograph of an artery that supplies the hear...

Micrograph of an artery that supplies the heart with significant atherosclerosis and marked luminal narrowing. Tissue has been stained using Masson’s trichrome. (Photo credit: Wikipedia)

Genomic Blueprint of the Heart

Scientists at the Gladstone Institutes have revealed the precise order and timing of hundreds of genetic “switches” required to construct a fully

  • functional heart from embryonic heart cells — providing new clues into the genetic basis for some forms of congenital heart disease.

In a study being published online today in the journal Cell, researchers in the laboratory of Gladstone Senior Investigator Benoit Bruneau, PhD,

  • employed stem cell technology, next-generation DNA sequencing and computing tools to piece together the instruction manual, or “genomic
  • blueprint” for how a heart becomes a heart. These findings offer renewed hope for combating life-threatening heart defects such as arrhythmias (irregular heart beat) and ventricular septal defects (“holes in the heart”).

ScienceDaily (Sep. 13, 2012)

They approach heart formation with a wide-angle lens by

  • looking at the entirety of the genetic material that gives heart cells their unique identity.

The news comes at a time of emerging importance for the biological process called “epigenetics,” in which a non-genetic factor impacts a cell’s genetic

  • makeup early during development — but sometimes with longer-term consequences. All of the cells in an organism contain the same DNA, but the
  • epigenetic instructions encoded in specific DNA sequences give the cell its identity. Epigenetics is of particular interest in heart formation, as the
  • incorrect on-and-off switching of genes during fetal development can lead to congenital heart disease — some forms of which may not be apparent until adulthood.

the scientists took embryonic stem cells from mice and reprogrammed them into beating heart cells by mimicking embryonic development in a petri dish. Next, they extracted the DNA from developing and mature heart cells, using an advanced gene-sequencing technique called ChIP-seq that lets scientists “see” the epigenetic signatures written in the DNA.

Map of Heart Disease Death Rates in US White M...

Map of Heart Disease Death Rates in US White Males from 2000-2004 (Photo credit: Wikipedia)

Estimated propability of death or non-fatal my...

Estimated propability of death or non-fatal myocardial-infarction over one year corresponding ti selectet values of the individual scores. Ordinate: individual score, abscissa: Propability of death or non-fatal myocardial infarction in 1 year (in %) (Photo credit: Wikipedia)

simply finding these signatures was only half the battle — we next had to decipher which aspects of heart formation they encoded

To do that, we harnessed the computing power of the Gladstone Bioinformatics Core. This allowed us to take the mountains of data collected from

  • gene sequencing and organize it into a readable, meaningful blueprint for how a heart becomes a heart.” Map the Genomic Blueprint of the Heart.  ScienceDaily.

Performance of transcription factor identification tools from differential gene expression data

A three step process is a clear way to establish belief in the performance of transcription factor identification tools

  • from differential gene expression data.
  • identify several types of differential gene expression data sets where the stimulus or trigger is clearly know
  • identify the transcription factors most likely associated with the sets expression data.
  • perform an upstream analysis from the identified transcription factor.

If the transcription factor and upstream analysis tools can trace the signal cascade back to the stimulus, the tools are

  • clearly producing relevant results, and belief in the performance of the analysis tools is established.

At this point, the tools can be directed with confidence to more challenging analyses such as

  • developed resistance or pathway elucidation.

The performance of IPA‘s new Transcription Factor and Upstream analysis tools was evaluated on the following datasets (processing details below):

  • TGFb stimulation, 1 hour, A549 lung adenocarcinoma cell line
  • BMP2 stimulation, 1 hour, Mouse Embryonic Stem Cell E14Tg2A.4
  • TNFa stimulation, 1 hour primary murine hepatocytes

For each of the above datasets, an upstream analysis from the identified transcription factors correctly identified the stimulus. IPA’s tools were very

  • easy to use and the
  • analysis time for the above experiments was less than one minute.

The performance, speed, and ease of use can only be characterized as very good, perhaps leading to breakthroughs when extended and used creatively. Ingenuity’s new transcription factor analysis tool in IPA, coupled with Ingenuity’s established upstream grow tools,  should be strongly considered for every lab analyzing differential expression data.

Differential expression data was obtained from CEL files using the Matlab functions:

affyrma, genelowvalfilter, genevarfilter, mattest, and mavolcanoplot.

Rick Stanton, Pathway Analysis Consultant

3. miR-200a regulates Nrf2 activation by targeting Keap1 mRNA in breast cancer cells.

Eades G, Yang M, Yao Y, Zhang Y, Zhou Q. J Biol Chem. 2011 Nov 25;286(47):40725-33. Epub 2011 Sep 16. regulates Nrf2 activation by targeting Keap1 mRNA in breast cancer cells.

NF-E2-related factor 2 (Nrf2) is an important transcription factor that

  • activates the expression of cellular detoxifying enzymes.

Nrf2 expression is largely regulated through the association of Nrf2 with Kelch-like ECH-associated protein 1 (Keap1), which

  • results in cytoplasmic Nrf2 degradation.

Conversely, little is known concerning the regulation of Keap1 expression. Until now, a regulatory role for microRNAs (miRs) in controlling Keap1 gene expression had not been characterized. By using miR array-

  • based screening, we observed miR-200a silencing in breast cancer cells and
  • demonstrated that upon re-expression, miR-200a
  • targets the Keap1 3′-untranslated region (3′-UTR), leading to Keap1 mRNA degradation. Loss of this regulatory mechanism may
  • contribute to the dysregulation of Nrf2 activity in breast cancer. Previously, we have identified epigenetic repression of miR-200a

in breast cancer cells. Here, we find that treatment with epigenetic therapy, the histone deacetylase inhibitor suberoylanilide hydroxamic acid, restored miR-200a expression and reduced Keap1 levels. This reduction in Keap1 levels corresponded with

  • Nrf2 nuclear translocation
  • and activation of Nrf2-dependent NAD(P)H-quinone oxidoreductase 1 (NQO1) gene transcription.

Moreover, we found that Nrf2 activation inhibited the anchorage-independent growth of breast cancer cells. Finally, our in vitro observations were confirmed in a model of carcinogen-induced mammary hyperplasia in vivo. In conclusion, our study demonstrates

  • that miR-200a regulates the Keap1/Nrf2 pathway in mammary epithelium, and we find that epigenetic therapy can restore miR-200a
  • regulation of Keap1 expression,
  • reactivating the Nrf2-dependent antioxidant pathway in breast cancer.

Nuclear factor-like 2  (erythroid-derived 2, also known as NFE2L2 or Nrf2, is a transcription factor that in humans is encoded by the NFE2L2 gene.[1])  NFE2L2 induces the expression of various genes including those that encode for several antioxidant enzymes, and it may play a physiological role in the regulation of oxidative stress. Investigational drugs that target NFE2L2 are of interest as potential therapeutic interventions for

  • oxidative-stress related pathologies.

4. Highly active zinc finger nucleases by extended modular assembly

MS Bhakta, IM Henry, DG Ousterout, KT Das, et al.  Corresponding author; email: active zinc finger nucleases by extended modular assembly

Zinc finger nucleases (ZFNs) are important tools for genome engineering. Despite intense interest by many academic groups,

  • the lack of robust non-commercial methods has hindered their widespread use. The modular assembly (MA) of ZFNs from
  • publicly-available one-finger archives provides a rapid method to create proteins that can recognize a very broad spectrum of DNA sequences.

However, three- and four-finger arrays often fail to produce active nucleases. Efforts to improve the specificity of the one-finger archives have not increased the success rate above 25%, suggesting that the MA method might

  • be inherently inefficient due to its insensitivity to context-dependent effects.

Here we present the first systematic study on the effect of array length on ZFN activity.  ZFNs composed of six-finger MA arrays produced mutations at 15 of 21 (71%) targeted

  • loci in human and mouse cells. A novel Drop-Out Linker scheme was used to rapidly assess three- to six-finger combinations,
  • demonstrating that shorter arrays could improve activity in some cases. Analysis of 268 array variants revealed that half of

MA ZFNs of any array composition that exceed an ab initio

  • B-score cut-off of 15 were active.
  • MA ZFNs are able to target more DNA sequences with higher success rates than other methods.

This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date
After six months, it is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported License), as described at modular_assembly/


These insightful reviews are based on the strategic data and insights from Thomson Reuters Cortellis™ for Competitive Intelligence.  (A Review of April-June 2012). INNOVATION: PERSONALIZED MEDICINE IN THE PIPELINE/ Cortellis™ for Competitive Intelligence/APRIL-JUNE 2012

The majority of diseases are complex and multi-factorial, involving multiple genes interacting with environmental factors. At the genetic level,

  • information from genome-wide association studies that elucidate common patterns of genetic variation across various human populations,
  • in addition to profiling, technologies can be utilized in discovery research to provide snapshots of genes and expression profiles that are controlled
  • by the same regulatory mechanism and are altered between healthy and diseased states.

The characterization of genes that are abnormally expressed in disease tissues could further be employed as

  • diagnostic markers,
  • prognostic indicators of efficacy and/or toxicity, or as
  • targets for therapeutic intervention.

As the defining catalyst that exponentially paved the way for personalized medicine, information from the published genome sequence revealed that much of the genetic variations in humans are concentrated in about 0.1 percent of the over 3 billion base pairs in the haploid DNA. Most of these variations involve substitution of a single nucleotide for another at a given location in the genetic sequence, known as single nucleotide polymorphism (SNP).

  • Combinations of linked SNPs aggregate together to form haplotypes and
  • together these serve as markers for locating genetic variations in DNA sequences.

SNPs located within the protein-coding region of a gene or within the control regions of DNA that regulate a gene’s activity could

  • have a substantial effect on the encoded protein and thus influence phenotypic outcomes.

Analyzing SNPs between patient population cohorts could highlight specific genotypic variations which can be correlated with specific phenotypic variations in disease predisposition and drug responses.

Prior to the genomic revolution, many of the established therapies were directed against less than 500 drug targets, with many of the top selling drugs acting on well defined protein pathways. However, the sequencing of the human genome has massively expanded the pool of molecular targets that could be exploited in unmet medical needs and currently, of the approximately 22,300 protein-coding genes in the human code, it has been estimated that up to 3000 are druggable. Furthermore, genomic technologies such as

  • high-throughput sequencing
  • and transcription profiling,

can be used to identify and validate biologically relevant target molecules, or can be applied to cell-based and mice disease models or directly to in vivo human tissues,

  • helping to correlate gene targets with phenotypic traits of complex diseases.

This is particularly important, as

  • insufficient validation of target gene/proteins in complex diseases may be a contributing factor in the decline in R&D productivity.

Personalized medicine no doubt is already having a tremendous impact on drug development pipelines. According to a study conducted by the Tufts Center for the Study of Drug Development, more than 90 percent of biopharmaceutical companies now utilize at least some

  • genomics-derived targets in their drug discovery programs.

However, pipeline analysis from Cortellis for Competitive Intelligence suggests that there is still a scientific gap that has resulted in difficulty optimizing these novel genomic targets into the clinical R&D portfolios of major pharmaceutical companies, particularly outside the oncology field. Selected examples of personalized medicine product candidates in clinical development include (see TABLE 4).

Table 4: Selected Personalized Medicines in Clinical Development
(DATA are Derived from Cortellis for Competitive Intelligence & Thomson Reuters IntegritySM)
http://Thomson for Competitive Intelligence/IntegritySM/Table_4_Selected_Personalized_Medicines_in_Clinical_Development/


The paucity of actual targeted therapy examples, especially outside oncology, suggest

  • that integration of the personalized medicine paradigm into biopharmaceutical R&D is still fraught with challenges.

Despite the fact that the Human genome Project has been completed for over ten years, the broader application of genomics with drug development

  • still remains unrealized, and is hampered by a number of scientific challenges. One of the major obstacles stems from
  • incomplete association of genomic alterations with complex disease pathways and the phenotypic consequences.

As the modality of most complex diseases are multi-factorial, understanding how each genomic driver event plays a role in disease and the

  • interaction/interdependence with other genetic and environmental factors is important for
  • determining the rationale for targeted prevention or treatment of the disease.

Mutations found in Melanomas may shed light on Cancer Growth

Gina Kolata. New York Times.

Mutations in Melanoma are in regions that control genes, not in the genes themselves. The mutations are exactly the type caused by exposure to ultraviolet light.  The findings are reported in two papers in

The findings do not suggest new treatments, but they help explain how melanomas – and possibly – other cancers – develop and what drives their growth. This is a modification found in the “dark matter”, according to Dr. Levi A. Garraway,  the 99 percent of DNA in a region that regulates genes. A small control region was mutated in 7 out of 10 of the tumors, commonly of one or two tiny changes.
A German Team led by Rajiv Kumar (Heidelberg) and Dirk Schadendorf (Essen) looked at a family whose members tended to get melanomas.  Their findings indicate that those inherited with the mutations might be born with cells that have taken the first step toward cancer.
The mutations spur cells to make telomerase, that keeps the cells immortal by preventing them from losing the ends of their chromosome, the telomere. Abundant telomerase occurs in 90 percent of cancers, according to Immaculata De Vivo at Harvard Medical School.
The importance of the findings is that the mechanism of telomerase involvement in cancer is now within view. But it is not clear how to block the telomerase production in cancer cells.
A slight mutation in the matched nucleotides c...

A slight mutation in the matched nucleotides can lead to chromosomal aberrations and unintentional genetic rearrangement. (Photo credit: Wikipedia)


This discussion addresses the issues raised about the direction to follow in personalized medicine. Despite the amount of work necessary to bring the clarity that is sought after, the experiments and experimental design is most essential.

  • The arrest of ciliogenesis in ovarian cancer cell lines compared to wild type (WT) ovarian epithelial cells, and
  •  The link to suppressing ciliogenesis by AURA protein and CHFR at the base of the cilium, which disappears at mitosis or with proliferation.
  •  There is no accumulation by upregulation of PDGF under starvation by the cancer cells compared to the effect in WT OSE.

Here we have a systematic combination of signaling events tied to changes in putative biomarkers that occur synchronously in Ov cancer cell lines.

These changes are identified with changes in

  • proliferation,
  • loss of ciliary structure, and
  • proliferation.

In this described scenario,

  • WT OSE cells would be arrested, and
  • it appears that they would take the path to apoptosis (under starvation).

Even without more information, this cluster is what one wants to have in a “syndromic classification”. The information used to form the classification entails the identification of strong ‘signaling-related’ biomarkers. The Gli2 peptide has to be part of this.

In principle, a syndromic classification would be ideally expected to have no less than 64 classes. If the classification is “weak”, then the class frequencies would be close to what one would expect in the WT OSE. In this case, in reality,

  • several combinatorial classes would have low frequency, and
  • others would be quite high.

This obeys the classification rules established by feature identification, and the information gain described by Solomon Kullback and extended by Akaike.

Does this have to be the case for all different cancer types? I don’t think so. The cells are different in ontogenesis.  In this case, even the WT OSE have mesenchymal features and so, are not fully directed to epithelial expression.  This happens to be the case in actual anatomic expression of the ovary.  On the other hand, one would expect shared features of the

  • ovary,
  • testes,
  • thyroid,
  • adrenals, and
  • pituitary.

There is biochemical expression in terms of their synthetic function – TPN organs. I would have to put the liver into that broad class. Other organs – skeletal muscle & heart – transform substrate into energy or work.  (Where you might also put intestinal smooth muscle).

They have to have different biomarker expressions, even though they much less often don’t form neoplasms. (Bone is not just a bioenergetic force. It is maintained by muscle action. It forms sarcomas. But there has to be a balance between bone removal by osteoclasts and refill by osteoblasts.)

Viewpoint: What we have learned

  1. The Watson-Crick model proposed in 1953 is limited for explaining fully genome effects
  2. The Pauling triplex model may have been prescient because of a more full anticipation of molecular bonding variants
  3. A more adequate triple-helix model has been proposed and is consistent with a compact genome in the nucleus

The structure of the genome is not as we assumed – based on the application of Fractal Geometry.  Current body of evidence is building that can reveal a more complete view of genome function.

  • transcription
  • cell regulation
  • mutations


I have just completed a most comprehensive review of the Human Genome Project. There are key research collaborations, problems in deciphering the underlying structure of the genome, and there are also both obstacles and insights to elucidating the complexity of the final model.

This is because of frequent observations of molecular problems in folding and other interactions between nucleotides that challenge the sufficiency of the original DNA model proposed by Watson and Crick. This has come about because of breakthrough innovation in technology and in computational methods.

Radoslav Bozov •

Molecular biology and growth was primarily initiated on biochemical structural paradigms aiming to define functional spatial dynamics of molecules via assignation of various types of bondings – covalent and non-covalent – hydrogen, ionic , dipole-dipole, hydrophobic interactions.

  • Lab techniques based on z/m paradigm allowed separation, isolation and identification of bio substances with a general marker identity finding correlation between physiological/cellular states.
  • The development of electronic/x-ray technologies allowed zooming in nano space without capturing time.
  • NMR technology identified the existence of space topology of initial and final atomic states giving a highly limited light on time – energy axis of atomic interactions.
  • Sequence technology and genomic perturbations shed light on uncertainty of genomic dynamics and regulators of functional ever expanding networks.
  • Transition state theory coupled to structural complexity identification and enzymatic mechanisms ran up parallel to work on various phenomena of strings of nucleotides (oligomers and polymers) – illusion/observation of constructing models on the dynamics of protein-dna-rna interference.
  • The physical energetic constrains of biochemistry were inapplicable in open biological systems. Biologists have accepted observation as a sole driver towards re-evaluating models.
  • The separation of matter and time constrains emerged as deviation of energy and space constrains transforming into the full acceptance of code theory of life. One simple thing was left unnoticed over time –
  • the amount of information of quantum matter within a single codon is larger than that of a single amino acid. This violated all physical laws/principles known to work with a limited degree of certainty.
  • The limited amount of information analyzed by conventional sequence identity led to the notion of applicability of statistical measures of and PCR technology. Mutations were identified over larger scale of data.
  • Quantum chemistry itself is being limited due discrete space/energy constrains, thus it transformed into concepts/principles in biology that possess highly limited physical values whatsoever.
  • The central dogma is partially broken as a result of
  1. regulatory constrains
  2. epigenetic phenomena and
  3. iRNA.

Large scale code computational data run into uncertainty of the processes of evolution and its consequence of signaling transformation. All drugs were ‘lucky based’ applicability and/or discovery with largely unpredictable side effect over time.

Other Related articles on this Open Access Online Sceintific Journal include the following:

Big Data in Genomic Medicine  lhb

BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair S Saha

Computational Genomics Center: New Unification of Computational Technologies at Stanford A Lev-Ari

Personalized medicine gearing up to tackle cancer ritu saxena

Differentiation Therapy – Epigenetics Tackles Solid Tumors sj Williams

Mechanism involved in Breast Cancer Cell Growth: Function in Early Detection & Treatment A Lev-Ari

The Molecular pathology of Breast Cancer Progression tilde barliya

Gastric Cancer: Whole-genome reconstruction and mutational signatures A Lev-Ari

Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1 ( A Lev-Ari        

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

Personalized Medicine: An Institute Profile – Coriell Institute for Medical Research: Part 3 A Lev-Ari

Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @ ALA Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders/

GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial” A Lev-Ari

Recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes in serous endometrial tumors S Saha

Personalized medicine-based cure for cancer might not be far away ritu saxena

Human Variome Project: encyclopedic catalog of sequence variants indexed to the human genome sequence A Lev-Ari

Prostate Cancer Cells: Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition sjwilliams

Inspiration From Dr. Maureen Cronin’s Achievements in Applying Genomic Sequencing to Cancer Diagnostics A Lev-Ari

The “Cancer establishments” examined by James Watson, co-discoverer of DNA w/Crick, 4/1953 A Lev-Ari

Directions for genomics in personalized medicine lhb

How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis. Sjwilliams

Mitochondria: More than just the “powerhouse of the cell” eritu saxena

Mitochondrial fission and fusion: potential therapeutic targets? Ritu saxena

Mitochondrial mutation analysis might be “1-step” away ritu saxena

mRNA interference with cancer expression lhb

Read Full Post »

Rewriting the Mathematics of Tumor Growth[1]; Teams Use Math Models to Sort Drivers from Passengers[2]:  Two JNCI Reviews by Mike Martin Regarding Genomics, Cancer, and Mutation

Curator: Stephen J. Williams, Ph.D.

Recently, there has been extensive interest in the cancer research and oncology community on detecting those mutations responsible for the initiation and propagation of a neoplastic cell (driver mutations) versus those mutations that are randomly (or by selective pressures) acquired due to the genetic instability of the transformed cell.  The impact of either type of mutation has been a topic for debate, with a recent article showing that some passenger mutations may actually be responsible for tumor survival.  In addition many articles, highlighted on this site (and referenced below) in recent years have described the importance of classifying driver and passenger mutations for the purposes of more effective personalized medicine strategies directed against tumors. Two review articles by Mike Martin in the Journal of the National Cancer Institute (JCNI) shed light on the current efforts and successes to discriminate between these passenger and driver mutations and determine impact of each type of mutation to tumor growth.  However, as described in the associated article, the picture is not as clear cut as previously thought and highlights some revolutionary findings. In Rewriting the Mathematics of Tumor Growth, researchers discovered that driver mutations may confer such a small growth advantage that, multiple mutations, including the so called passenger mutations are necessary in order to sustain tumor growth. In fact, much experimental evidence has suggested at least six defined genetic events may be necessary for the in-vitro transformation of human cells.  The following table shows some of the genetic events required for in-vitro transformation in cell culture systems.

Genetic events required for transformation

 Species  Cell type  # of genes required for tumor formation*  Genes used  Reference Events required for priming
Human FibroblastsEmbryonic kidney 3 hTERTH-rasLarge T (a)Hahn(Weinberg) 2LT+hTERT
Mammary epithelialMyoblastsEmbryonic kidney 6 hTERTH-rasP53DDc-myc

cyclin D1CDK4

(b)Kendall(Counter) Hras required for tumorigenesis so probably 5 events needed
Fibroblasts 4 Large TSmall TH-rashTERT (c)Sun(Hornsby) 2Large T + H-ras
Fibroblasts 4 Large TSmall ThTERTRas (d)Rangarajan(Weinberg) 3hTERT, Ras and either small or largeT
Keratinocytes 4 CyclinD1




(e)Goessel(Opitz) 3 for anchorage independence (cyclin D1, dnp53, EGFR),Cyclin D1+dnp53 for immortalization
HOSE 6 CDK4, cyclin D, hTERT plus combination of either P53DD, myrAkt, and H-ras or P53DD, H-ras, c-myc Bcl2 (f)Sasaki(Kiyono) 5
HOSE 3 hTERTSV40 earlyH-ras orK-ras (g)Liu(Bast) 2hTERT+ SV40 early
HOSE 3 Large ThTERTH-ras orc-erB-2 (h)Kusakari(Fujii) 2hTERT+large T
Rat Fibroblasts 2 Large TH-ras (i)Hirakawa Did not analyze
Fibroblasts 2 Large TH-ras (d)Rangarajan(Weinberg) Large T
Mouse MOSEIn p53-/- background 3 c-mycK-rasAkt (j)Orsulic
Pig Fibroblasts 6 p53DDhTERT

CDK4H-ras c-myc

cyclin D1

(k)Adam(Counter) 5 need all butp53DD

Note: priming means events required to immortalize but not fully transform.  * Note that both ability to form colonies in soft agarose and subsequently tested for tumor formation in immunocompromised mice.

a.         Hahn, W. C., Counter, C. M., Lundberg, A. S., Beijersbergen, R. L., Brooks, M. W., and Weinberg, R. A. (1999) Creation of human tumour cells with defined genetic elements, Nature 400, 464-468.

b.         Kendall, S. D., Linardic, C. M., Adam, S. J., and Counter, C. M. (2005) A network of genetic events sufficient to convert normal human cells to a tumorigenic state, Cancer Res 65, 9824-9828.

c.         Sun, B., Chen, M., Hawks, C. L., Pereira-Smith, O. M., and Hornsby, P. J. (2005) The minimal set of genetic alterations required for conversion of primary human fibroblasts to cancer cells in the subrenal capsule assay, Neoplasia 7, 585-593.

d.         Rangarajan, A., Hong, S. J., Gifford, A., and Weinberg, R. A. (2004) Species- and cell type-specific requirements for cellular transformation, Cancer Cell 6, 171-183.

e.         Goessel, G., Quante, M., Hahn, W. C., Harada, H., Heeg, S., Suliman, Y., Doebele, M., von Werder, A., Fulda, C., Nakagawa, H., Rustgi, A. K., Blum, H. E., and Opitz, O. G. (2005) Creating oral squamous cancer cells: a cellular model of oral-esophageal carcinogenesis, Proc Natl Acad Sci U S A 102, 15599-15604.

f.          Sasaki, R., Narisawa-Saito, M., Yugawa, T., Fujita, M., Tashiro, H., Katabuchi, H., and Kiyono, T. (2009) Oncogenic transformation of human ovarian surface epithelial cells with defined cellular oncogenes, Carcinogenesis 30, 423-431.

g.         Liu, J., Yang, G., Thompson-Lanza, J. A., Glassman, A., Hayes, K., Patterson, A., Marquez, R. T., Auersperg, N., Yu, Y., Hahn, W. C., Mills, G. B., and Bast, R. C., Jr. (2004) A genetically defined model for human ovarian cancer, Cancer Res 64, 1655-1663.

h.         Kusakari, T., Kariya, M., Mandai, M., Tsuruta, Y., Hamid, A. A., Fukuhara, K., Nanbu, K., Takakura, K., and Fujii, S. (2003) C-erbB-2 or mutant Ha-ras induced malignant transformation of immortalized human ovarian surface epithelial cells in vitro, Br J Cancer 89, 2293-2298.

i.          Hirakawa, T., and Ruley, H. E. (1988) Rescue of cells from ras oncogene-induced growth arrest by a second, complementing, oncogene, Proc Natl Acad Sci U S A 85, 1519-1523.

j.          Orsulic, S., Li, Y., Soslow, R. A., Vitale-Cross, L. A., Gutkind, J. S., and Varmus, H. E. (2002) Induction of ovarian cancer by defined multiple genetic changes in a mouse model system, Cancer Cell 1, 53-62.

k.         Adam, S. J., Rund, L. A., Kuzmuk, K. N., Zachary, J. F., Schook, L. B., and Counter, C. M. (2007) Genetic induction of tumorigenesis in swine, Oncogene 26, 1038-1045.

However it may be argued that the aforementioned experimental examples were produced in cell lines with a more stable genome than that which is seen in most tumors and had used traditional assays of transformation, such as growth in soft agarose and tumorigenicity in immunocompromised mice, as endpoints of transformation, and not representative of the tumor growth seen in the clinical setting.

Therefore Bert Vogelstein, M.D., along with collaborators around the world developed a model they termed the “sequential driver mutation theory”, in which they describe that driver mutations multiply over time with each mutation “slightly increasing the tumor growth rate through a process that depends on three factors”:

  1. Driver mutation rate
  2. The 0.4% selective growth advantage
  3. Cell division time

This model was based on a combination of experimental data and computer simulations of gliobastoma multiforme and pancreatic adenocarcinoma.  Most tumor models follow a Gompertz kinetics, which show how tumor growth is exponential but eventually levels off over time.

This new theory shows though that a tumor cell with only one driver mutation can only grow so much, until a second driver mutation is required.  Using data for the COSMIC database (Catalog of Somatic Mutations in Cancer) together with analysis software CHASM (Cancer-specific High-throughput Annotation of Somatic Mutations) the researchers analyzed 713 mutations sequenced from 14 glioma patients and 562 mutations in nine pancreatic adenocarcinomas, revealing at least 100 tumor suppressor genes and 100 oncogenes altered.  Therefore, the authors suggested these may be possible driver mutations, or at least mutations required for the sustained growth of these tumors.  Applying this new model to data obtained from Dr. Giardiello’s publication concerning familial adenopolypsis in New England Journal of medicine in 19993 and 2000, the sequential driver mutation model predicted age distribution of FAP patients, number and size of polyps, and polyp growth rate than previous models.  This surprising number of required driver mutations for full transformation was also verified in a study led by University of Texas Southwestern Medical Center biologist Jerry Shay, Ph.D., who noted “this team’s surprise nearly 45% of all colorectal candidate oncogenes (65 mutations) drove malignant proliferation”[3].

However, some investigators do not believe the model is complex enough to account for other factors involved in oncogenesis, such as epigenetic factors like methylation and acetylation.  In addition the review also discusses host and tissue factors which may complicate the models, such as location where a tumor develops.  However, most of the investigators interviewed for this review agreed that focusing on this long-term progression of the disease may give us clues to other potential druggable targets.

Teams Use Math Models to Sort Drivers From Passengers

A related review from Mike Martin in JNCI [2] describes a statistical method, published in 2009 Cancer Informatics[4], which distinguishes chromosomal abnormalities that can drive oncogenesis from passenger abnormalities.  Chromosomal abnormalities, such as deletions, additions, and translocations are common in cancer.  For instance, the well-known Philadelphia chromosome, a translocation between chromosome 9 and 22 which results in the BCR-ABL tyrosine kinase fusion protein is the molecular basis of chronic myelogenous leukemia.

In the report, Eytan Domany, Ph.D., from Weizmann Institute and several colleagues from University of Lausanne, University of Haifa and the Broad Institute were analyzing chromosomal aberrations in a subset of medulloblastoma, which had more gain and losses in chromosomes than had been attributed to the disease.  Using a statistical method they termed a “volumetric sieve”, the investigators were able to identify driver versus passenger aberrations based on three filters:

  • Fraction of patients with the abnormality
  • Length of DNA involved in the aberrant chromosome
  • Abnormality’s copy number

Another method to sort the most “important” chromosomal aberrations from less relevant alterations is termed GISTIC[5], as the website describes is: a tool to identify genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth (at the Broad Institute website  The method allows for comparison across multiple tumors so noise is eliminated and improves consistency of analysis.  This method had been successfully used to determine driver aberrations is mesotheliomas, leukemias, and identify new oncogenes in adenocarcinomas of the lung and squamous cell carcinoma of the esophagus.

Main references for the two Mike Martin articles are as follows:

1.         Martin M: Rewriting the mathematics of tumor growth. Journal of the National Cancer Institute 2011, 103(21):1564-1565.

2.         Martin M: Aberrant chromosomes: teams use math models to sort drivers from passengers. Journal of the National Cancer Institute 2010, 102(6):369-371.

3.         Eskiocak U, Kim SB, Ly P, Roig AI, Biglione S, Komurov K, Cornelius C, Wright WE, White MA, Shay JW: Functional parsing of driver mutations in the colorectal cancer genome reveals numerous suppressors of anchorage-independent growth. Cancer research 2011, 71(13):4359-4365.

4.         Shay T, Lambiv WL, Reiner-Benaim A, Hegi ME, Domany E: Combining chromosomal arm status and significantly aberrant genomic locations reveals new cancer subtypes. Cancer informatics 2009, 7:91-104.

5.         Beroukhim R, Getz G, Nghiemphu L, Barretina J, Hsueh T, Linhart D, Vivanco I, Lee JC, Huang JH, Alexander S et al: Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proceedings of the National Academy of Sciences of the United States of America 2007, 104(50):20007-20012.

Further posts on CANCER and GENOMICS and Sequencing published on the site include:

The Initiation and Growth of Molecular Biology and Genomics

Inaugural Genomics in Medicine – The Conference Program, 2/11-12/2013, San Francisco, CA

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

Breast Cancer: Genomic profiling to predict Survival: Combination of Histopathology and Gene Expression Analysis

Computational Genomics Center: New Unification of Computational Technologies at Stanford

GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial”

arrayMap: Genomic Feature Mining of Cancer Entities of Copy Number Abnormalities (CNAs) Data

Comprehensive Genomic Characterization of Squamous Cell Lung Cancers

Mosaicism’ is Associated with Aging and Chronic Diseases like Cancer: detection of genetic mosaicism could be an early marker for detecting cancer.

Additional references:

[1] Michor F, Iwasa Y, and Nowak MA (2004) Dynamics of cancer

progression. Nature Reviews Cancer 4, 197-205.

[2] Crespi B and Summers K (2005) Evolutionary biology of cancer.

Trends in Ecology and Evolution 20, 545-552.

[3] Merlo LMF, et al. (2006) Cancer as an evolutionary and ecological

process. Nature Reviews Cancer 6, 924-935.

[4] McFarland C, et al. “Accumulation of deleterious passenger mutations

in cancer,” in preparation.

[5] Birkbak NJ, et al. (2011) Paradoxical relationship between

chromosomal instability and survival outcome in cancer. Cancer

Research 71,3447-3452.

Read Full Post »

Directions for Genomics in Personalized Medicine

Author: Larry H. Bernstein, MD, FCAP


J. Craig Venter

J. Craig Venter (Photo credit: Wikipedia)

Otto Heinrich Warburg

Otto Heinrich Warburg (Photo credit: Wikipedia)


This discussion will identify the huge expansion of genomic technology in the search for  biopharmacotherapeutic targets that continue to be explored involving different levels and interacting signaling pathways.   There are several methods of analyzing gene expression that will be discussed. Great primary emphasis required investigation of combinations of mutations expressed in different cancer types.  James Watson has proposed a major hypothesis that expresses the need to focus on “central” “driver mutations” that correspond with the regulation of gene expression, cell proliferation, and cell metabolism eith a critical rejection of antioxiant benefits.  What hasn’t been know is why drug resistance develops and whether the cellular migration and aerobic glycolysis can be redirected after cell metastasis occurs.  I attempt to bring out the complexities of current efforts.


  • This discussion is a continuation of a previous discussion on the role of genomics if discovery of therapeutic targets for cancer, each somewhat different, but all related to:
  • The reversal of carcinoma by targeting a key driver of multiple signaling pathways that activate cell proliferation
  • Pinpointing a stage in a multistage process at which tumor progression links to changes in morphology from basal cells to invasive carcinoma with changes in polarity and loss of glandular architecture
  • Reversal of the carcinoma through using a small molecule that either is covalently bound to a nanoparticle delivery system that blocks or reverses tumor development
  • Synthesis of a small molecule that interacts with the translation of the genome either by substitution of a key driver molecule or by blocking at the mRNA stage of translation
  • Blocking more than one signaling pathway that are links to carcinogenesis and cellular proliferation and invasion

Difficulty of the problem

A problem expressed by James Watson is that the investigations that are ongoing

  • are following a pathway that is not driven by attacking the “primary” driver of carcinogenesis.

He uses the Myc gene as an example, as noted in the previous discussion. The problem may be more complicated than he envisions.

  • The most consistent problem in chemotherapy, irrespective of the design and the target has been cancer remission for a short time followed by recurrence, and then
  • switching to another drug, or combination chemotherapy.

It is common to “clean” the field at the time of resection using radiotherapy before chemotherapy.

  • But the goal is understood to be “palliation”, not cure.

This raises a serious issue in the hypothesis posed by Watson. The issue is

  • whether there is a core locus of genetic regulation that is common to carcinogenesis irrespective of tissue metabolic expression.
  • This is supported by the observation that tissue specific express is lost in cancer cells by de-differentiation.

Historical Perspective


In 1967 Otto Warburg published his view in a paper “The prime cause and prevention of cancer”.
There are primary and secondary causes of all diseases

  • plague – primary: plague bacillus
  • plague – secondary: filth, rats, and fleas

cancer, above all diseases,

  • has countless seconday causes
  • primary – replacement of respiration of oxygen in normal body tissue by fermentation of glucose with conversion from obligate aerobic to anaerobic, as in bacterial cells

The cornerstone to understanding cancer is in study of the energetics of life

This thinking came out of decades of work in the Dahlem Institute Kaiser Wilhelm pre WWII and Max Planck Institute after WWII, supported by the Rockefeller Foundation.

  • The oxygen- and hydrogen-transferring enzymes were discovered and isolated.
  • The methods were elegant for that time, using a manometer that improved on the method used by Haldane, that did not allow the leakage of O2 or CO2.
  • The interest was initiated by the increased growth of Sea Urchin eggs after fertization, which turned out to be not comparable to the rapid growth of cancer cells.
  • Warburg used both normal and cancer tissue and measured the utilization of O2. He found
    • that the normal tissue did not accumulate lactic acid.
    • Cancer tissue generated lactic acid
    • the rate of O2 consumption the same as normal tissue, but
    • the rate of lactate formation far exceeded any tissue, except the retina.
    • This was a discovery studied by “Pasteur” 60 years earlier (facultative aerobes), which he called the Pasteur effect.
    • Hematopoietic cells of bone marrow develop aerobic glycolysis when exposed to a low oxygen condition.

He then followed on an observation by Otto Meyerhoff (Embden-Myerhoff cycle) that in muscle

  • the consumption of one molecule of oxygen generates two molecules of lactate, but in aerobic glycolysis, the relationship disappears.
  • He expressed the effectiveness of respiration by the ‘Meyerhoff quotient’.
  • He found that cancer cells didn’t have a quotient of ‘2’

The role of the allosteric enzyme phosphofructokinase (PFK) not then known, would tie together the glycolytic and gluconeogenic pathways.
He used a heavy metal ion chelator ethylcarbylamine to

  • sever the link and turn on aerobic glycolysis.

The explanation for this was provided years later by the work fleshed out by Lynen, Bucher, Lowry, Racker, and Sols.

  • The rate-limiting enzyme, PFK is regulated by the concentrations of ATP, ADP, and inorganic phosphate. The ethylcarbylamide was an ‘uncoupler’ of oxidative phosphorylation.

Warburg understood that when normal cells switched to aerobic glycolysis

  • it is a re-orientation of normal cell expression.
  • this provides the basis for the inference that neoplastic cells become more like each other than their cell of origin.
  • embryonic cells can be transformed into cancer cells under hypoxic conditions
  • re-exposure to higher oxygen did not cause reversion back to normal cells.

Warburg publically expressed the rejected view in 1954 (at age 83) that restriction of chemical wastes, food additives, and air pollution would substantially reduce cancer rates.

His emphasis on the impairment of respiration was inadequate.

  • the prevailing view today is loss of controlled growth of normal cells in cancer cells.

Otto Warburg: Cell Physiologist, Biochemist, and Eccentric. Hans Krebs, in collaboration with Roswitha Schmid. Clarendon Press, Oxford. 1991.ISBN 0-19-858171-8.

The Human Genome Project

The Human Genome Project, driven by Francis Collins at NIH, and by Craig Venter at the Institute for Genome Research (TIGR) had parallel projects to map the human chromosome, completed in 2003. It originally aimed to map the nucleotides contained in a human haploid reference genome (more than three billion). TIGR was the first complete genomic sequencing of a free living organism, Haemophilus influenzae, in 1995. This used a shotgun sequencing technique pioneered earlier, but which had never been used for a whole bacterium.
Venter broke away from the HGP and started Celera in 1998 because of resistance to the shotgun sequency method, and his team completed the genome sequence in three years – seven years’ less time than the HGP timetable (using the gene of Dr. Venter). TIGR eventually sequenced and analyzed more than 50 microbial genomes. Its bioinformatics group developed

  • pioneering software algorithms that were used to analyze these genomes,
  • including the automatic gene finder GLIMMER and
  • the sequence alignment program MUMmer.

In 2002, Venter created and personally funded the J. Craig Venter Institute (JCVI) Joint Technology Center (JTC), which specialized in high throughput sequencing.  The JTC, in the top ranks of scientific institutions worldwide, sequenced nearly 100 million base pairs of DNA per day for its affiliated institutions (JCVI) .

He received his his Ph.D. degree in physiology and pharmacology from the University of California, San Diego in 1975 under biochemist Nathan O. Kaplan. A full professor at the State University of New York at Buffalo, he joined the National Institutes of Health in 1984. There he learned of a technique for rapidly identifying all of the mRNAs present in a cell and began to use it to identify human brain genes. The short cDNA sequence fragments discovered by this method are called expressed sequence tags (ESTs), a name coined by Anthony Kerlavage at TIGR.
Venter believed that shotgun sequencing was the fastest and most effective way to get useful human genome data. There was a belief that shotgun sequencing was less accurate than the clone-by-clone method chosen by the HGP, but the technique became widely accepted by the scientific community and is still the de facto standard used today.


Shreeve, James (2004). The Genome War: How Craig Venter Tried to Capture the Code of Life and Save the World. Knopf. ISBN 0375406298.
Sulston, John (2002). The Common Thread: A Story of Science, Politics, Ethics and the Human Genome. Joseph Henry Press. ISBN 0309084091.
“The Human Genome Project Race”. Center for Biomolecular Science & Engineering, UC Santa Cruz. Retrieved 20 March 2012.
Venter, J. Craig (2007). A Life Decoded: My Genome: My Life. Viking Adult. ISBN 0670063584.

Use of a Fluorophor Probe

An article has been discussed by Dr.  Tilda Barilya on use of a sensitive fluorescent probe in the near IR spectrum at > 700 nm to identify malignant ovarian cells in-vivo in abdominal exploration by tagging an overexpressed FR-α (folate-FITA)
The author makes the point that:

  • In ovarian cancer, the FR-α appears to constitute a good target because it is overexpressed in 90–95% of malignant tumors, especially serous carcinomas.
  • Targeting ligand, folate, is attractive as it is nontoxic, inexpensive and relatively easily conjugated to a fluorescent dye to create a tumor-specific fluorescent contrast agent.
  • The report is identified as “ the first in-human proof-of-principle of the use of intraoperative tumor-specific fluorescence imaging in staging and debulking surgery for ovarian cancer using the systemically administered targeted fluorescent agent folate-FITC.”

While this does invoke possibilities for prognosis, the decision to perform the surgery, whether laparoscopic or open, is late in the discovery process. However, it does suggest the possibility that the discovery and the treatment might be combined if the biomarker itself had the fluorescence to identify the overexpression, but it also is combined with a tag to block the overexpession. This hypothetical possibility is now expressed below.

Gene Editing

Dr. Aviva Lev-Ari reports that a new technique developed at MIT Broad Institute and the Rockefeller University can edit DNA in precise locations taken from Science News titled Editing Genome With High Precision: New Method to Insert Multiple Genes in Specific Locations, Delete Defective Genes”.

Using this system, scientists can alter

  • several genome sites simultaneously and
  • can achieve much greater control over where new genes are inserted

According to Feng Zhang, this is an improvement beyond splicing the gene in specific locations and insertion of complexes difficult to assemble known as transcription activator-like effector nucleases (TALENs).

  • The researchers create DNA-editing complexes
  • using naturally occurring bacterial protein-RNA systems
  • that recognize and snip viral DNA, including
  • a nuclease called Cas9 bound to short RNA sequences.
  • they target specific locations in the genome, and
  • when they encounter a match, Cas9 cuts the DNA.

This approach can be used either to

  • disrupt the function of a gene or
  • to replace it with a new one.
  • To replace the gene, a DNA template for the new gene has to be copied into the genome after the DNA is cut. The method is also very precise —
  • if there is a single base-pair difference between the RNA targeting sequence and the genome sequence, Cas9 is not activated.

In its first iteration, it appears comparable in efficiency to what zinc finger nucleases and TALENs have to offer.
The research team has deposited the necessary genetic components with a nonprofit called Addgene, and they have also created a website with tips and tools for using this new technique.
The above story is reprinted from materials provided by Massachusetts Institute of Technology. The original article was written by Anne Trafton.
Le Cong, F. Ann Ran, David Cox, Shuailiang Lin, Robert Barretto, Naomi Habib, Patrick D. Hsu, Xuebing Wu, Wenyan Jiang, Luciano Marraffini, and Feng Zhang. Multiplex Genome Engineering Using CRISPR/Cas Systems. Science, 3 January 2013 DOI: 10.1126/science.1231143. Editing genome with high precision: New method to insert multiple genes in specific locations, delete defective genes. ScienceDaily. Retrieved January 20, 2013, from­ /releases/2013/01/130103143205.htm?goback=%2Egde_4346921_member_205356312.

Dr. Lev-Ari also reports on a study of early detection of breast cancer in “Mechanism involved in Breast Cancer Cell Growth: Function in Early Detection & Treatment“, by Dr. Rotem Karni and PhD student Vered Ben Hur at the Institute for Medical Research Israel-Canada of the Hebrew University.
These researchers have discovered a new mechanism by which breast cancer cells switch on their aggressive cancerous behavior. The discovery provides a valuable marker for the early diagnosis and follow-up treatment of malignant growths.
The method they use is

  • RNA splicing and insertion.
  • The information needed for the production of a mature protein is encoded in segments called exons .
  • In the splicing process, the non-coding segments of the RNA (introns) are spliced from the pre-mRNA and
  • the exons are joined together.

Alternative splicing is when a specific ”scene” (or exon) is either inserted or deleted from the movie (mRNA), thus changing its meaning.

  • Over 90 percent of the genes in our genome undergo alternative splicing of one or more of their exons, and
  • the resulting changes in the proteins encoded by these different mRNAs are required for normal function.
  • the normal process of alternative splicing is altered in cancer, and
  • ”bad” protein forms are generated that aid cancer cell proliferation and survival.

The researchers reported in online Cell Reports that breast cancer cells

  • change the alternative splicing of an important enzyme, called S6K1, which is
  • a protein involved in the transmission of information into the cell.
  • when this happens, breast cancer cells start to produce shorter versions of this enzyme and
  • these shorter versions transmit signals ordering the cells to grow, proliferate, survive and invade other tissues (otherwise proliferation is suppressed)

The application to biotherapeutics would be to ”reverse” the alternative splicing of S6K1 in cancer cells back to the normal situation as a novel anti-cancer therapy.

Additional Developments:

A*STAR Scientists Pinpoint Genetic Changes that Spell Cancer: Fruit flies light the way for scientists to uncover genetic changes.

With a new approach, researchers may rapidly distinguish the range of

  • genetic changes that are causally linked to cancer (i.e. “driver” mutations)
  • versus those with limited impact on cancer progression.

This study published in the prestigious journal Genes & Development could pave the way to design more targeted treatment against different cancer types, based on the specific cancer-linked mutations present in the patient, an advance in the development of personalized medicine.

Signaling pathways involved in tumour formation are conserved from fruit flies to humans. In fact, about 75 percent of known human disease genes have a recognizable match in the genome of fruit flies.
Leveraging on their genetic similarities, Dr Hector Herranz, a post-doctorate from the Dr. Stephen Cohen’s team developed an innovative strategy to genetically screen the whole fly genome for “cooperating” cancer genes.

  • These genes appear to have little or no impact on cancer.
  • However, they cooperate with other cancer genes, so that
  • the combination causes aggressive cancer, which
  • neither would cause alone.

In this study, the team was specifically looking for genes that

  • could cooperate with EGFR “driver” mutation,
  • a genetic change commonly associated with breast and lung cancers in humans.
  • SOCS5 (reported in this paper) is one of the several new “cooperating” cancer genes to be identified.

Already, there are indications that levels of SOCS5 expression are

  • reduced in breast cancer, and
  • patients with low levels of SOCS5 have poor prognosis.”

The IMCB team is preparing to explore the use of SOCS5 as a biomarker in diagnosis for cancer.

Probing What Fuels Cancer

‘Altered cellular metabolism is a hallmark of cancer,’ says Dr Patrick Pollard, in the Nuffield Department of Clinical Medicine at Oxford. Most cancer cells get the energy they need predominantly through a high rate of glycolysis, allowing cancer cells deal with the low oxygen levels that tend to be present in a tumour.

But whether dysfunctional metabolism causes cancer, as Warburg believed, or is something that happens afterwards is a different question. In the meantime, gene studies rapidly progressed and indicated that genetic changes occur in cancer.

DNA mutations spring up all the time in the body’s cells, but

  • most are quickly repaired.
  • Alternatively the cell might shut down or be killed off (apoptosis) before any damage is caused. However, the repair machinery is not perfect.
  • If changes occur that bypass parts of the repair machinery or sabotage it,
  • the cell can escape the body’s normal controls on growth and
  • DNA changes can begin to accumulate as the cell becomes cancerous.

Patrick believes certain changes in cells can’t always be accounted for by ‘genetics.’
He is now collaborating with Professor Tomoyoshi Soga’s large lab at Keio University in Japan, which has been at the forefront of developing the technology for metabolomics research over the past couple of decades.

The Japanese lab’s ability to

  • screen samples for thousands of compounds and metabolites at once, and
  • the access to tumour material and cell and animal models of disease
  • enables them to probe the metabolic changes that occur in cancer.

There is reason to believe that

  • dysfunctional cell metabolism is important in cancer.
  • genes with metabolic functions are associated with some cancers
  • changes in the function of a metabolic enzyme have been implicated in the development of gliomas.

These results have led to the idea that some metabolic compounds, or metabolites, when they accumulate in cells, can cause changes to metabolic processes and set cells off on a path towards cancer.

Patrick Pollard and colleagues have now published a perspective article in the journal Frontiers in Molecular and Cellular Oncology that proposes fumarate as such an ‘oncometabolite’. Fumarate is a standard compound involved in cellular metabolism.

The researchers summarize evidence that shows how

  • accumulation of fumarate when an enzyme goes wrong affects various biological pathways in the cell.
  • It shifts the balance of metabolic processes and disrupts the cell in ways that could favour development of cancer.

Patrick and colleagues write in their latest article that the shift in focus of cancer research to include cancer cell metabolism ‘has highlighted how woefully ignorant we are about the complexities and interrelationships of cellular metabolic pathways’.

Extensive Promoter-Centered Chromatin Interactions Provide a Topological Basis for Transcription Regulation
(Li G, Ruan X, Auerbach RK, Sandhu KS, et al.) Cell 2012; 148(1-2): 84-98.

Using genome-wide Chromatin Interaction Analysis with Paired-End-Tag sequencing (ChIA-PET),
mapped long-range chromatin interactions associated with RNA polymerase II in human cells
uncovered widespread promoter-centered intragenic, extragenic, and intergenic interactions.

  • These interactions further aggregated into higher-order clusters
  • proximal and distal genes were engaged through promoter-promoter interactions.
  • most genes with promoter-promoter interactions were active and transcribed cooperatively
  • some interacting promoters could influence each other implying combinatorial complexity of transcriptional controls.

Comparative analyses of different cell lines showed that

  • cell-specific chromatin interactions could provide structural frameworks for cell-specific transcription,
  • and suggested significant enrichment of enhancer-promoter interactions for cell-specific functions.
  • genetically-identified disease-associated noncoding elements were spatially engaged with corresponding genes through long-range interactions.

Overall, our study provides insights into transcription regulation by

  • three-dimensional chromatin interactions for both housekeeping and
  • cell-specific genes in human cells.

New Nucleoporin: Regulator of Transcriptional Repression and Beyond.

(NJ Sarma and K Willis) Nucleus 2012; 3(6): 1–8; © 2012 Landes Bioscience

Transcriptional regulation is a complex process that requires the integrated action of many multi-protein complexes.
The way in which a living cell coordinates the action of these complexes in time and space is still poorly understood.

  • nuclear pores, well known for their role in 3′ processing and export of transcripts, also participate in the control of transcriptional initiation.
  • nuclear pores interface with the well-described machinery that regulates initiation.

This work led to the discovery that

  • specific nucleoporins are required for binding of the repressor protein Mig1 to its site in target promoters.
  • Nuclear pores are involved in repressing, as well as activating, transcription.

Here we discuss in detail the main models explaining our result and consider what each implies about the roles that nuclear pores play in the regulation of gene expression.

Prediction of Breast Cancer Metastasis by Gene Expression Profiles: A Comparison of Metagenes and Single Genes.

(M Burton, M Thomassen, Q Tan, and TA Kruse.) Cancer Informatics 2012:11 193–217 doi: 10.4137/CIN.S10375

The popularity of a large number of microarray applications has in cancer research led to the development of predictive or prognostic gene expression profiles. However, the diversity of microarray platforms has made the full validation of such profiles and their related gene lists across studies difficult and, at the level of classification accuracies, rarely validated in multiple independent datasets. Frequently, while the individual genes between such lists may not match, genes with same function are included across such gene lists. Development of such lists does not take into account the fact that

  • genes can be grouped together as metagenes (MGs) based on common characteristics such as pathways, regulation, or genomic location.

In this study we compared the performance of either metagene- or single gene-based feature sets and classifiers using random forest and two support vector machines for classifier building. The performance

  • within the same dataset,
  • feature set validation perfor­mance, and
  • validation performance of entire classifiers in strictly independent datasets

were assessed by

  • 10 times repeated 10-fold cross validation,
  • leave-one-out cross validation, and
  • one-fold validation, respectively.

To test the significance of the performance difference between MG- and SG-features/classifiers, we used a repeated down-sampled binomial test approach.

MG- and SG-feature sets are transferable and perform well for training and testing prediction of metastasis outcome

  • in strictly independent data sets, both
  • between different and
  • within similar microarray platforms, while
  • classifiers had a poorer performance when validated in strictly independent datasets.

The study showed that MG- and SG-feature sets perform equally well in classifying indepen­dent data. Furthermore, SG-classifiers significantly outperformed MG-classifier

  • when validation is conducted between datasets using similar platforms, while
  • no significant performance difference was found when validation was performed between different platforms.

Prediction of metastasis outcome in lymph node–negative patients by MG- and SG-classifiers showed that SG-classifiers performed significantly better than MG-classifiers when validated in independent data based on the same microarray platform as used for developing the classifier. However, the MG- and SG-classifiers had similar performance when conducting classifier validation in independent data based on a different microarray platform. The latter was also true when only validating sets of MG- and SG-features in independent datasets, both between and within similar and different platforms.

Identification and Insilico Analysis of Retinoblastoma Serum microRNA Profile and Gene Targets Towards Prediction of Novel Serum Biomarkers.

M Beta, A Venkatesan, M Vasudevan, U Vetrivel, et al. Identification and Insilico Analysis of Retinoblastoma Serum microRNA Profile and Gene Targets Towards Prediction of Novel Serum Biomarkers.

http://Bioinformatics and Biology Insights 2013:7 21–34. doi: 10.4137/BBI.S10501

This study was undertaken

  • to identify the differentially expressed miRNAs in the serum of children with RB in comparison with the normal age matched serum,
  • to analyze its concurrence with the existing RB tumor miRNA profile,
  • to identify its novel gene targets specific to RB, and
  • to study the expression of a few of the identified oncogenic miRNAs in the advanced stage primary RB patient’s serum sample.

MiRNA profiling performed on 14 pooled serum from chil­dren with advanced RB and 14 normal age matched serum samples

  • 21 miRNAs found to be upregulated (fold change > 2.0, P < 0.05) and
  • 24 downregulated (fold change > 2.0, P < 0.05).

Intersection of 59 significantly deregulated miRNAs identified from RB tumor profiles with that of miRNAs detected in serum profile revealed that

  • 33 miRNAs had followed a similar deregulation pattern in RB serum.

Later we validated a few of the miRNAs (miRNA 17-92) identified by microarray in the RB patient serum samples (n = 20) by using qRT-PCR.

Expression of the oncogenic miRNAs, miR-17, miR-18a, and miR-20a by qRT-PCR was significant in the serum samples

  • exploring the potential of serum miRNAs identification as noninvasive diagnosis.

Moreover, from miRNA gene target prediction, key regulatory genes of

  • cell proliferation,
  • apoptosis, and
  • positive and negative regulatory networks

involved in RB progression were identified in the gene expression profile of RB tumors.
Therefore, these identified miRNAs and their corresponding target genes could give insights on

  • potential biomarkers and key events involved in the RB pathway.

Computational Design of Targeted Inhibitors of Polo-Like Kinase 1 ( lk1).

(KS Jani and DS Dalafave) Bioinformatics and Biology Insights 2012:6 23–31.doi: 10.4137/BBI.S8971.

Computational design of small molecule putative inhibitors of Polo-like kinase 1 (Plk1) is presented. Plk1, which regulates the cell cycle, is often over expressed in cancers.

  • Down regulation of Plk1 has been shown to inhibit tumor progression.
  • Most kinase inhibitors interact with the ATP binding site on Plk1, which is highly conserved.
  • This makes the development of Plk1-specific inhibitors challenging, since different kinases have similar ATP sites.

However, Plk1 also contains a unique region called the polo-box domain (PBD), which is absent from other kinases.

  • the PBD site was used as a target for designed Plk1 putative inhibitors.
  • Common structural features of several experimentally known Plk1 ligands were first identified.
  • The findings were used to design small molecules that specifically bonded Plk1.
  • Drug likeness and possible toxicities of the molecules were investigated.
  • Molecules with no implied toxicities and optimal drug likeness values were used for docking studies.
  • Several molecules were identified that made stable complexes only with Plk1 and LYN kinases, but not with other kinases.
  • One molecule was found to bind exclusively the PBD site of Plk1.

Possible utilization of the designed molecules in drugs against cancers with over expressed Plk1 is discussed.


The previous discussions reviewed the status of an evolving personalized medicine multicentered and worldwide enterprise.  It is also clear from these reports that the search for targeted drugs matched to a cancer profile or signature has identified several approaches that show great promise.

  • We know considerably  more about metabolic pathways and linked changes in transcription that occur in neoplastic development.
  • There are several methods used to do highly accurate  insertions in gene sequences that are linked to specific metabolic changes, and
  • some may have significant implications for therapeutics, if
    • the link is a change that is associated with a driver mutation
    • the link can be identified by a fluorescent or other probe
    • the link is tied to a mRNA or peptide product that is a biomarker measured in the circulation
  • We have probes to genetic links to the control of many and interacting signaling pathways.
  • We know more about transcription through mRNA.
  • We are closer to the possibility that metabolic substrates, like ‘fumarate’ (a key intermediate in the TCA cycle), may provide a means to reverse regulate the neoplastic cells.
  • We may also find metabolic channels that drive the cells from proliferation to apoptosis or normal activity.


This discussion identified the huge expansion of genomic technology in the investigation of biopharmacotherapeutic targets that have been identified involving different levels and interacting signaling pathways.   There are several methods of analyzing gene expression, and a primary emphasis is given to combinations of mutations expressed in different cancer types.  There is a major hypothesis that expresses the need to focus on “central” “driver mutations” that correspond with the regulation of gene expression, cell proliferation, and cell metabolism.  What hasn’t been know is why drug resistance develops and whether the cellular migration and aerobic glycolysis can be redirected after cell metastasis occurs.


A slight mutation in the matched nucleotides c...

A slight mutation in the matched nucleotides can lead to chromosomal aberrations and unintentional genetic rearrangement. (Photo credit: Wikipedia)

Deutsch: Regulation der Phosphofructokinase

Deutsch: Regulation der Phosphofructokinase (Photo credit: Wikipedia)

Additional Related articles

Other posts related to this discussion were published on this Open Source  Online Scientific Journal from Leaders in Pharmaceutical Business  Intelligence:

Big Data in Genomic Medicine, LHB

A New Therapy for Melanoma, LHB

BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair,  S Saha

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

Computational Genomics Center: New Unification of Computational Technologies at Stanford, A. Lev-Ari

Ovarian Cancer and fluorescence-guided surgery: A report, T.  Barliya

Personalized medicine gearing up to tackle cancer ,  R. Saxena

Exploring the role of vitamin C in Cancer therapy,   R. Saxena

Differentiation Therapy – Epigenetics Tackles Solid Tumors,    SJ Williams

Mechanism involved in Breast Cancer Cell Growth: Function in Early Detection & Treatment,   A. Lev-Ari

Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS),  A. Lev-Ari

Role of Primary Cilia in Ovarian Cancer,  A. Awan

The Molecular Pathology of Breast Cancer Progression,  T. Bailiya`

Stanniocalcin: A Cancer Biomarker,   A. Awan

Nanotechnology, personalized medicine and DNA sequencing,  T. Barliya

Gastric Cancer: Whole-genome reconstruction and mutational signatures,  A. Lev-Ari

Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1, A. Lev-Ari

LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2,  A. Lev-Ari

Personalized Medicine: An Institute Profile – Coriell Institute for Medical Research: Part 3, A. Lev-Ari

The Consumer Market for Personal DNA Sequencing: Part 4, A. Lev-Ari

Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @   A. Lev-Ari

GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial”  A Lev-Ari

Recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes in serous endometrial tumors,  S. Saha

Metabolic drivers in aggressive brain tumors,  pkandala

Personalized medicine-based cure for cancer might not be far away, R. Saxena

Response to Multiple Cancer Drugs through Regulation of TGF-β Receptor Signaling: a MED12 Control, A. Lev-Ari

Human Variome Project: encyclopedic catalog of sequence variants indexed to the human genome sequence,  A. Lev-Ari

Prostate Cancer Cells: Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition,  SJ Williams

Tumor Imaging and Targeting: Predicting Tumor Response to Treatment: Where we stand?, R. Saxena

Nanotechnology: Detecting and Treating metastatic cancer in the lymph node, T. Barliya

Heroes in Medical Research: Barnett Rosenberg and the Discovery of Cisplatin, SJ Williams

Inspiration From Dr. Maureen Cronin’s Achievements in Applying Genomic Sequencing to Cancer Diagnostics,  A. Lev-Ari

The “Cancer establishments” examined by James Watson, co-discoverer of DNA w/Crick, 4/1953,      A. Lev-Ari

Nanotech Therapy for Breast Cancer. T. Barlyia

Dasatinib in Combination With Other Drugs for Advanced, Recurrent Ovarian Cancer,  pkandala

Squeezing Ovarian Cancer Cells to Predict Metastatic Potential: Cell Stiffness as Possible Biomarker, pkandala

Hypothesis – following on James Watson,  LHB…ts-are-harmful/

Otto Warburg, A Giant of Modern Cellular Biology, LHB

Is the Warburg Effect the cause or the effect of cancer: A 21st Century View?  LHB

Remembering a Great Scientist among Mentors,  LHB

Portrait of a great scientist and mentor: Nathan Oram Kaplan,   LHB

Predicting Tumor Response, Progression, and Time to Recurrence, LHB

Directions for genomics in personalized medicine,   LHB

How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis,  Sjwilliams

Novel Cancer Hypothesis Suggests Antioxidants Are Harmful, LHB

Mitochondria: Origin from oxygen free environment, role in aerobic glycolysis, metabolic adaptation,  LHB

Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets, LHB

Cancer Innovations from across the Web, LHB

Mitochondrial Damage and Repair under Oxidative Stress, LHB

Mitochondria: More than just the “powerhouse of the cell” R. Saxena

Mitochondria and Cancer: An overview of mechanisms, R. Saxena

Mitochondrial fission and fusion: potential therapeutic targets?  R. Saxena

Mitochondrial mutation analysis might be “1-step” away, R. Saxena

β Integrin emerges as an important player in mitochondrial dysfunction associated Gastric Cancer,       R. Saxena

mRNA interference with cancer expression, LHB

What can we expect of tumor therapeutic response?  LHB

Expanding the Genetic Alphabet and linking the genome to the metabolome, LHB

Breast Cancer, drug resistance, and biopharmaceutical targets, LHB

Breast Cancer: Genomic Profiling to Predict Survival: Combination of Histopathology and Gene Expression Analysis, A. Lev-Ari

Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis,   LHB

Identification of Biomarkers that are Related to the Actin Cytoskeleton, LHB

Nitric Oxide has a ubiquitous role in the regulation of glycolysis -with a concomitant influence on mitochondrial function, LHB

Genomic Analysis: FLUIDIGM Technology in the Life Science and Agricultural Biotechnology,  A. Lev-Ari

Nanotechnology: Detecting and Treating metastatic cancer in the lymph node, T. Barliya


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