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Follow-up on Tomosynthesis

Posted in Bio Instrumentation in Experimental Life Sciences Research, Biomarkers & Medical Diagnostics, FDA, CE Mark & Global Regulatory Affairs: process management and strategic planning - GCP, GLP, ISO 14155, Imaging-based Cancer Patient Management, Medical Device Therapies for Altzheimer’s Disease, Medical Devices R&D Investment, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Statistical Methods for Research Evaluation, tagged 2d mammography, 3d mammography, biomarkers, breast cancer, breast cancer screening, Cancer - General, Cancer screening, companion diagnostics, dense breast tissue, imaging based screening, imaging biomarkers, Medical imaging, Medical technology, personalised treatment, Personalized medicine, research, tomosynthesis on July 11, 2013| 2 Comments »

Follow-up on Tomosynthesis

Writer & Curator: Dror Nir, PhD

Tomosynthesis, is a method for performing high-resolution limited-angle (i.e. not full 360⁰ rotation but more like ~50⁰) tomography. The use of such systems in breast-cancer screening is steadily increasing following the clearance of such system by the FDA on 2011; see my posts – Improving Mammography-based imaging for better treatment planning and State of the art in oncologic imaging of breast.

Many radiologists expects that Tomosynthesis will eventually replace conventional mammography due to the fact that it increases the sensitivity of breast cancer detection. This claim is supported by new peer-reviewed publications. In addition, the patient’s experience during Tomosynthesis is less painful due to a lesser pressure that is applied to the breast and while presented with higher in-plane resolution and less imaging artifacts the mean glandular dose of digital breast Tomosynthesis is comparable to that of full field digital mammography. Because it is relatively new, Tomosynthesis is not available at every hospital. As well, the procedure is recognized for reimbursement by public-health schemes.

A good summary of radiologist opinion on Tomosynthesis can be found in the following video:

Recent studies’ results with digital Tomosynthesis are promising. In addition to increase in sensitivity for detection of small cancer lesions researchers claim that this new breast imaging technique will make breast cancers easier to see in dense breast tissue. Here is a paper published on-line by the Lancet just a couple of months ago:

***Integration of 3D digital mammography with* *tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study***

Stefano Ciatto†, Nehmat Houssami, Daniela Bernardi, Francesca Caumo, Marco Pellegrini, Silvia Brunelli, Paola Tuttobene, Paola Bricolo, Carmine Fantò, Marvi Valentini, Stefania Montemezzi, Petra Macaskill , Lancet Oncol. 2013 Jun;14(7):583-9. doi: 10.1016/S1470-2045(13)70134-7. Epub 2013 Apr 25.

Background Digital breast tomosynthesis with 3D images might overcome some of the limitations of conventional 2D mammography for detection of breast cancer. We investigated the effect of integrated 2D and 3D mammography in population breast-cancer screening.

Methods Screening with Tomosynthesis OR standard Mammography (STORM) was a prospective comparative study. We recruited asymptomatic women aged 48 years or older who attended population-based breast-cancer screening through the Trento and Verona screening services (Italy) from August, 2011, to June, 2012. We did screen-reading in two sequential phases—2D only and integrated 2D and 3D mammography—yielding paired data for each screen. Standard double-reading by breast radiologists determined whether to recall the participant based on positive mammography at either screen read. Outcomes were measured from final assessment or excision histology. Primary outcome measures were the number of detected cancers, the number of detected cancers per 1000 screens, the number and proportion of false positive recalls, and incremental cancer detection attributable to integrated 2D and 3D mammography. We compared paired binary data with McNemar’s test.

Findings 7292 women were screened (median age 58 years [IQR 54–63]). We detected 59 breast cancers (including 52 invasive cancers) in 57 women. Both 2D and integrated 2D and 3D screening detected 39 cancers. We detected 20 cancers with integrated 2D and 3D only versus none with 2D screening only (p<0.0001). Cancer detection rates were 5·3 cancers per 1000 screens (95% CI 3.8–7.3) for 2D only, and 8.1 cancers per 1000 screens (6.2–10.4) for integrated 2D and 3D screening. The incremental cancer detection rate attributable to integrated 2D and 3D mammography was 2.7 cancers per 1000 screens (1.7–4.2). 395 screens (5.5%; 95% CI 5.0–6.0) resulted in false positive recalls: 181 at both screen reads, and 141 with 2D only versus 73 with integrated 2D and 3D screening (p<0·0001). We estimated that conditional recall (positive integrated 2D and 3D mammography as a condition to recall) could have reduced false positive recalls by 17.2% (95% CI 13.6–21.3) without missing any of the cancers detected in the study population.

Interpretation Integrated 2D and 3D mammography improves breast-cancer detection and has the potential to reduce false positive recalls. Randomised controlled trials are needed to compare integrated 2D and 3D mammography with 2D mammography for breast cancer screening.

Funding National Breast Cancer Foundation, Australia; National Health and Medical Research Council, Australia; Hologic, USA; Technologic, Italy.

Introduction

Although controversial, mammography screening is the only population-level early detection strategy that has been shown to reduce breast-cancer mortality in randomised trials.^1,2 Irrespective of which side of the mammography screening debate one supports,^1–3 efforts should be made to investigate methods that enhance the quality of (and hence potential benefit from) mammography screening. A limitation of standard 2D mammography is the superimposition of breast tissue or parenchymal density, which can obscure cancers or make normal structures appear suspicious. This short coming reduces the sensitivity of mammography and increases false-positive screening. Digital breast tomosynthesis with 3D images might help to overcome these limitations. Several reviews^4,5 have described the development of breast tomosynthesis technology, in which several low-dose radiographs are used to reconstruct a pseudo-3D image of the breast.^4–6

Initial clinical studies of 3D mammography, ^6–10 though based on small or selected series, suggest that addition of 3D to 2D mammography could improve cancer detection and reduce the number of false positives. However, previous assessments of breast tomosynthesis might have been constrained by selection biases that distorted the potential effect of 3D mammography; thus, screening trials of integrated 2D and 3D mammography are needed.⁶

We report the results of a large prospective study (Screening with Tomosynthesis OR standard Mammography [STORM]) of 3D digital mammography. We investigated the effect of screen-reading using both standard 2D and 3D imaging with tomosynthesis compared with screening with standard 2D digital mammography only for population breast-cancer screening.

Methods

Study design and participants

STORM is a prospective population-screening study that compares mammography screen-reading in two sequential phases (figure)—2D only versus integrated 2D and 3D mammography with tomosynthesis—yielding paired results for each screening examination. Women aged 48 years or older who attended population-based screening through the Trento and Verona screening services, Italy, from August, 2011, to June, 2012, were invited to be screened with integrated 2D and 3D mammography. Participants in routine screening mammography (once every 2 years) were asymptomatic women at standard (population) risk for breast cancer. The study was granted institutional ethics approval at each centre, and participants gave written informed consent. Women who opted not to participate in the study received standard 2D mammography. Digital mammography has been used in the Trento breast-screening programme since 2005, and in the Verona programme since 2007; each service monitors outcomes and quality indicators as dictated by European standards, and both have published data for screening performance.11,12

Procedures

All participants had digital mammography using a Selenia Dimensions Unit with integrated 2D and 3D mammography done in the COMBO mode (Hologic, Bedford, MA, USA): this setting takes 2D and 3D images at the same screening examination with a single breast position and compression. Each 2D and 3D image consisted of a bilateral two-view (mediolateral oblique and craniocaudal) mammogram. Screening mammograms were interpreted sequentially by radiologists, first on the basis of standard 2D mammography alone, and then by the same radiologist (on the same day) on the basis of integrated 2D and 3D mammography (figure). Thus, integrated 2D and 3D mammography screening refers to non-independent screen reading based on joint interpretation of 2D and 3D images, and does not refer to analytical combinations. Radiologists had to record whether or not to recall the participant at each screen-reading phase before progressing to the next phase of the sequence. For each screen, data were also collected for breast density (at the 2D screen-read), and the side and quadrant for any recalled abnormality (at each screen-read). All eight radiologists were breast radiologists with a mean of 8 years (range 3–13 years) experience in mammography screening, and had received basic training in integrated 2D and 3D mammography. Several of the radiologists had also used 2D and 3D mammography for patients recalled after positive conventional mammography screening as part of previous studies of tomosynthesis.8,13

Mammograms were interpreted in two independent screen-reads done in parallel, as practiced in most population breast-screening programs in Europe. A screen was considered positive and the woman recalled for further investigations if either screen-reader recorded a positive result at either 2D or integrated 2D and 3D screening (figure). When previous screening mammograms were available, these were shown to the radiologist at the time of screen-reading, as is standard practice. For assessment of breast density, we used Breast Imaging Reporting and Data System (BI-RADS)¹⁴ classification, with participants allocated to one of two groups (1–2 [low density] or 3–4 [high density]). Disagreement between readers about breast density was resolved by assessment by a third reader.

Our primary outcomes were the number of cancers detected, the number of cancers detected per 1000 screens, the number and percentage of false positive recalls, and the incremental cancer detection rate attributable to integrated 2D and 3D mammography screening. We compared the number of cancers that were detected only at 2D mammography screen-reading and those that were detected only at 2D and 3D mammography screen-reading; we also did this analysis for false positive recalls. To explore the potential effect of integrated 2D and 3D screening on false-positive recalls, we also estimated how many false-positive recalls would have resulted from using a hypothetical conditional false-positive recall approach; – i.e. positive integrated 2D and 3D mammography as a condition of recall (screening recalled at 2D mammography only would not be recalled). Pre-planned secondary analyses were comparison of outcome measures by age group and breast density.

Outcomes were assessed by excision histology for participants who had surgery, or the complete assessment outcome (including investigative imaging with or without histology from core needle biopsy) for all recalled participants. Because our study focuses on the difference in detection by the two screening methods, some cancers might have been missed by both 2D and integrated 2D and 3D mammography; this possibility could be assessed at future follow-up to identify interval cancers. However, this outcome is not assessed in the present study and does not affect estimates of our primary outcomes – i.e. comparative true or false positive detection for 2D-only versus integrated 2D and 3D mammography.

Statistical analysis

The sample size was chosen to provide 80% power to detect a difference of 20% in cancer detection, assuming a detection probability of 80% for integrated 2D and 3D screening mammography and 60% for 2D only screening, with a two-sided significance threshold of 5%. Based on the method of Lachenbruch¹⁵ for estimating sample size for studies that use McNemar’s test for paired binary data, a minimum of 40 cancers were needed. Because most screens in the participating centres were incident (repeat) screening (75%–80%), we used an underlying breast-cancer prevalence of 0·5% to estimate that roughly 7500–8000 screens would be needed to identify 40 cancers in the study population.

We calculated the Wilson CI for the false-positive recall ratio for integrated 2D and 3D screening with conditional recall compared with 2D only screening.¹⁶ All of the other analyses were done with SAS/STAT (version 9.2), using exact methods to compute 95 CIs and p-values.

Role of the funding source

The sponsors of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author (NH) had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Results

7292 participants with a median age of 58 years (IQR 54–63, range 48–71) were screened between Aug 12, 2011, and June 29, 2012. Roughly 5% of invited women declined integrated 2D and 3D screening and received standard 2D mammography. We present data for 7294 screens because two participants had bilateral cancer (detected with different screen-reading techniques for one participant). We detected 59 breast cancers in 57 participants (52 invasive cancers and seven ductal carcinoma in-situ). Of the invasive cancers, most were invasive ductal (n=37); others were invasive special types (n=7), invasive lobular (n=4), and mixed invasive types (n=4).

Table 1 shows the characteristics of the cancers. Mean tumour size (for the invasive cancers with known exact size) was 13.7 mm (SD 5.8) for cancers detected with both 2D alone and integrated 2D and 3D screening (n=29), and 13.5 mm (SD 6.7) for cancers detected only with integrated 2D and 3D screening (n=13).

Of the 59 cancers, 39 were detected at both 2D and integrated 2D and 3D screening (table 2). 20 cancers were detected with only integrated 2D and 3D screening compared with none detected with only 2D screening (p<0.0001; table 2). 395 screens were false positive (5.5%, 95% CI 5.0–6.0); 181 occurred at both screen-readings, and 141 occurred at 2D screening only compared with 73 at integrated 2D and 3D screening (p<0.0001; table 2). These differences were still significant in sensitivity analyses that excluded the two participants with bilateral cancer (data not shown).

5.3 cancers per 1000 screens (95% CI 3.8–7.3; table 3) were detected with 2D mammography only versus 8.1 cancers per 1000 screens (95% CI 6.2–10.4) with integrated 2D and 3D mammography (p<0.0001). The incremental cancer detection rate attributable to integrated 2D and 3D screening was 2.7 cancers per 1000 screens (95% CI 1.7–4.2), which is 33.9% (95% CI 22.1–47.4) of the cancers detected in the study population. In a sensitivity analysis that excluded the two participants with bilateral cancer the estimated incremental cancer detection rate attributable to integrated 2D and 3D screening was 2.6 cancers per 1000 screens (95% CI 1.4–3.8). The stratified results show that integrated 2D and 3D mammography was associated with an incrementally increased cancer detection rate in both age-groups and density categories (tables 3–5). A minority (16.7%) of breasts were of high density (category 3–4) reducing the power of statistical comparisons in this subgroup (table 5). The incremental cancer detection rate was much the same in low density versus high density groups (2.8 per 1000 vs 2.5 per 1000; p=0.84; table 3).

Overall recall—any recall resulting in true or false positive screens—was 6.2% (95% CI 5.7–6.8), and the false-positive rate for the 7235 screens of participants who did not have breast cancer was 5.5% (5.0–6.0). Table 6 shows the contribution to false-positive recalls from 2D mammography only, integrated 2D and 3D mammography only, and both, and the estimated number of false positives if positive integrated 2D and 3D mammography was a condition for recall (positive 2D only not recalled). Overall, more of the false-positive rate was driven by 2D mammography only than by integrated 2D and 3D, although almost half of the false-positive rate was a result of false positives recalled at both screen-reading phases (table 6). The findings were much the same when stratified by age and breast density (table 6). Had a conditional recall rule been applied, we estimate that the false-positive rate would have been 3.5% (95% CI 3.1–4.0%; table 6) and could have potentially prevented 68 of the 395 false positives (a reduction of 17.2%; 95% CI 13.6–21.3). The ratio between the number of false positives with integrated 2D and 3D screening with conditional recall (n=254) versus 2D only screening (n=322) was 0.79 (95% CI 0.71–0.87).

Discussion

Our study showed that integrated 2D and 3D mammography screening significantly increases detection of breast cancer compared with conventional mammography screening. There was consistent evidence of an incremental improvement in detection from integrated 2D and 3D mammography across age-group and breast density strata, although the analysis by breast density was limited by low number of women with breasts of high density.

One should note that we investigated comparative cancer detection, and not absolute screening sensitivity. By integrating 2D and 3D mammography using the study screen-reading protocol, 1% of false-positive recalls resulted from 2D and 3D screen-reading only (table 6). However, significantly more false positives resulted from 2D only mammography compared with integrated 2D and 3D mammography, both overall and in the stratified analyses. Application of a conditional recall rule would have resulted in a false-positive rate of 3.5% instead of the actual false-positive rate of 5.5%. The estimated false positive recall ratio of 0.79 for integrated 2D and 3D screening with conditional recall compared with 2D only screening suggests that integrated 2D and 3D screening could reduce false recalls by roughly a fifth. Had such a condition been adopted, none of the cancers detected in the study would have been missed because no cancers were detected by 2D mammography only, although this result might be because our design allowed an independent read for 2D only mammography whereas the integrated 2D and 3D read was an interpretation of a combination of 2D and 3D imaging. We do not recommend that such a conditional recall rule be used in breast-cancer screening until our findings are replicated in other mammography screening studies—STORM involved double-reading by experienced breast radiologists, and our results might not apply to other screening settings. Using a test set of 130 mammograms, Wallis and colleagues⁷ report that adding tomosynthesis to 2D mammography increased the accuracy of inexperienced readers (but not of experienced readers), therefore having experienced radiologists in STORM could have underestimated the effect of integrated 2D and 3D screen-reading.

No other population screening trials of integrated 2D and 3D mammography have reported final results (panel); however, an interim analysis of the Oslo trial¹⁷ a large population screening study has shown that integrated 2D and 3D mammography substantially increases detection of breast cancer. The Oslo study investigators screened women with both 2D and 3D mammography, but randomised reading strategies (with vs without 3D mammograms) and adjusted for the different screen-readers,¹⁷whereas we used sequential screen-reading to keep the same reader for each examination. Our estimates for comparative cancer detection and for cancer detection rates are consistent with those of the interim analysis of the Oslo study.¹⁷ The applied recall methods differed between the Oslo study (which used an arbitration meeting to decide recall) and the STORM study (we recalled based on a decision by either screen-reader), yet both studies show that 3D mammography reduces false-positive recalls when added to standard mammography.

An editorial in The Lancet¹⁸ might indeed signal the closing of a chapter of debate about the benefits and harms of screening. We hope that our work might be the beginning of a new chapter for mammography screening: our findings should encourage new assessments of screening using 2D and 3D mammography and should factor several issues related to our study. First, we compared standard 2D mammography with integrated 2D and 3D mammography the 3D mammograms were not interpreted independently of the 2D mammograms therefore 3D mammography only (without the 2D images) might not provide the same results. Our experience with breast tomosynthesis and a review⁶ of 3D mammography underscore the importance of 2D images in integrated 2D and 3D screen-reading. The 2D images form the basis of the radiologist’s ability to integrate the information from 3D images with that from 2D images. Second, although most screening in STORM was incident screening, the substantial increase in cancer detection rate with integrated 2D and 3D mammography results from the enhanced sensitivity of integrated 2D and 3D screening and is probably also a result of a prevalence effect (ie, the effect of a first screening round with integrated 2D and 3D mammography). We did not assess the effect of repeat (incident) screening with integrated 2D and 3D mammography on cancer detection it might provide a smaller effect on cancer detection rates than what we report. Third, STORM was not designed to measure biological differences between the cancers detected at integrated 2D and 3D screening compared with those detected at both screen-reading phases. Descriptive analyses suggest that, generally, breast cancers detected only at integrated 2D and 3D screening had similar features (eg, histology, pathological tumour size, node status) as those detected at both screen-reading phases. Thus, some of the cancers detected only at 2D and 3D screening might represent early detection (and would be expected to receive screening benefit) whereas some might represent over-detection and a harm from screening, as for conventional screening mam mography.1,19 The absence of consensus about over-diagnosis in breast-cancer screening should not detract from the importance of our study findings to applied screening research and to screening practice; however, our trial was not done to assess the extent to which integrated 2D and 3D mammography might contribute to over-diagnosis.

The average dose of glandular radiation from the many low-dose projections taken during a single acquisition of 3D mammography is roughly the same as that from 2D mammography.^6,20–22 Using integrated 2D and 3D entails both a 2D and 3D acquisition in one breast compression, which roughly doubles the radiation dose to the breast. Therefore, integrated 2D and 3D mammography for population screening might only be justifiable if improved outcomes were not defined solely in terms of improved detection. For example, it would be valuable to show that the increased detection with integrated 2D and 3D screening leads to reduced interval cancer rates at follow-up. A limitation of our study might be that data for interval cancers were not available; however, because of the paired design we used, future evaluation of interval cancer rates from our study will only apply to breast cancers that were not identified using 2D only or integrated 2D and 3D screening. We know of two patients from our study who have developed interval cancers (follow-up range 8–16 months). We did not get this information from cancer registries and follow-up was very short, so these data should be interpreted very cautiously, especially because interval cancers would be expected to occur in the second year of the standard 2 year interval between screening rounds. Studies of interval cancer rates after integrated 2D and 3D mammography would need to be randomised controlled trials and have a very large sample size. Additionally, the development of reconstructed 2D images from a 3D mammogram²³ provides a timely solution to concerns about radiation by providing both the 2D and 3D images from tomosynthesis, eliminating the need for two acquisitions.

We have shown that integrated 2D and 3D mammography in population breast-cancer screening increases detection of breast cancer and can reduce false-positive recalls depending on the recall strategy. Our results do not warrant an immediate change to breast-screening practice, instead, they show the urgent need for randomised controlled trials of integrated 2D and 3D versus 2D mammography, and for further translational research in breast tomosynthesis. We envisage that future screening trials investigating this issue will include measures of breast cancer detection, and will be designed to assess interval cancer rates as a surrogate endpoint for screening efficacy.

Contributors

SC had the idea for and designed the study, and collected and interpreted data. NH advised on study concepts and methods, analysed and interpreted data, searched the published work, and wrote and revised the report. DB and FC were lead radiologists, recruited participants, collected data, and commented on the draft report. MP, SB, PT, PB, PT, CF, and MV did the screen-reading, collected data, and reviewed the draft report. SM collected data and reviewed the draft report. PM planned the statistical analysis, analysed and interpreted data, and wrote and revised the report.

Conflicts of interest

SC, DB, FC, MP, SB, PT, PB, CF, MV, and SM received assistance from Hologic (Hologic USA; Technologic Italy) in the form of tomosynthesis technology and technical support for the duration of the study, and travel support to attend collaborators’ meetings. NH receives research support from a National Breast Cancer Foundation (NBCF Australia) Practitioner Fellowship, and has received travel support from Hologic to attend a collaborators’ meeting. PM receives research support through Australia’s National Health and Medical Research Council programme grant 633003 to the Screening & Test Evaluation Program.

References

1 Independent UK Panel on Breast Cancer Screening. The benefits and harms of breast cancer screening: an independent review. Lancet 2012; 380: 1778–86.

2 Glasziou P, Houssami N. The evidence base for breast cancer screening. Prev Med 2011; 53: 100–102.

3 Autier P, Esserman LJ, Flowers CI, Houssami N. Breast cancer screening: the questions answered. Nat Rev Clin Oncol 2012; 9: 599–605.

4 Baker JA, Lo JY. Breast tomosynthesis: state-of-the-art and review of the literature. Acad Radiol 2011; 18: 1298–310.

5 Helvie MA. Digital mammography imaging: breast tomosynthesis and advanced applications. Radiol Clin North Am 2010; 48: 917–29.

6 Houssami N, Skaane P. Overview of the evidence on digital breast tomosynthesis in breast cancer detection. Breast 2013; 22: 101–08.

7 Wallis MG, Moa E, Zanca F, Leifland K, Danielsson M. Two-view and single-view tomosynthesis versus full-field digital mammography: high-resolution X-ray imaging observer study. Radiology 2012; 262: 788–96.

8 Bernardi D, Ciatto S, Pellegrini M, et al. Prospective study of breast tomosynthesis as a triage to assessment in screening. Breast Cancer Res Treat 2012; 133: 267–71.

9 Michell MJ, Iqbal A, Wasan RK, et al. A comparison of the accuracy of film-screen mammography, full-field digital mammography, and digital breast tomosynthesis. Clin Radiol 2012; 67: 976–81.

10 Skaane P, Gullien R, Bjorndal H, et al. Digital breast tomosynthesis (DBT): initial experience in a clinical setting. Acta Radiol 2012; 53: 524–29.

11 Pellegrini M, Bernardi D, Di MS, et al. Analysis of proportional incidence and review of interval cancer cases observed within the mammography screening programme in Trento province, Italy. Radiol Med 2011; 116: 1217–25.

12 Caumo F, Vecchiato F, Pellegrini M, Vettorazzi M, Ciatto S, Montemezzi S. Analysis of interval cancers observed in an Italian mammography screening programme (2000–2006). Radiol Med 2009; 114: 907–14.

13 Bernardi D, Ciatto S, Pellegrini M, et al. Application of breast tomosynthesis in screening: incremental effect on mammography acquisition and reading time. Br J Radiol 2012; 85: e1174–78.

14 American College of Radiology. ACR BI-RADS: breast imaging reporting and data system, Breast Imaging Atlas. Reston: American College of Radiology, 2003.

15 Lachenbruch PA. On the sample size for studies based on McNemar’s test. Stat Med 1992; 11: 1521–25.

16 Bonett DG, Price RM. Confidence intervals for a ratio of binomial proportions based on paired data. Stat Med 2006; 25: 3039–47.

17 Skaane P, Bandos AI, Gullien R, et al. Comparison of digital mammography alone and digital mammography plus tomosynthesis in a population-based screening program. Radiology 2013; published online Jan 3. http://dx.doi.org/10.1148/ radiol.12121373.

18 The Lancet. The breast cancer screening debate: closing a chapter? Lancet 2012; 380: 1714.

19 Biesheuvel C, Barratt A, Howard K, Houssami N, Irwig L. Effects of study methods and biases on estimates of invasive breast cancer overdetection with mammography screening: a systematic review. Lancet Oncol 2007; 8: 1129–38.

20 Tagliafico A, Astengo D, Cavagnetto F, et al. One-to-one comparison between digital spot compression view and digital breast tomosynthesis. Eur Radiol 2012; 22: 539–44.

21 Tingberg A, Fornvik D, Mattsson S, Svahn T, Timberg P, Zackrisson S. Breast cancer screening with tomosynthesis—initial experiences. Radiat Prot Dosimetry 2011; 147: 180–83.

22 Feng SS, Sechopoulos I. Clinical digital breast tomosynthesis system: dosimetric characterization. Radiology 2012; 263: 35–42.

23 Gur D, Zuley ML, Anello MI, et al. Dose reduction in digital breast tomosynthesis (DBT) screening using synthetically reconstructed projection images: an observer performance study. Acad Radiol 2012; 19: 166–71.

A very good and down-to-earth comment on this article was made by Jules H Sumkin who disclosed that he is an unpaid member of SAB Hologic Inc and have a PI research agreement between University of Pittsburgh and Hologic Inc.

“ The results of the study by Stefano Ciatto and colleagues¹ are consistent with recently published prospective,^2,3 retrospective,⁴ and observational⁵ reports on the same topic. The study¹ had limitations, including the fact that the same radiologist interpreted screens sequentially the same day without cross-balancing which examination was read first. Also, the false-negative findings for integrated 2D and 3D mammography, and therefore absolute benefit from the procedure, could not be adequately assessed because cases recalled by 2D mammography alone (141 cases) did not result in a single detection of an additional cancer while the recalls from the integrated 2D and 3D mammography alone (73 cases) resulted in the detection of 20 additional cancers. Nevertheless, the results are in strong agreement with other studies reporting of substantial performance improvements when the screening is done with integrated 2D and 3D mammography.

I disagree with the conclusion of the study with regards to the urgent need for randomised clinical trials of integrated 2D and 3D versus 2D mammography. First, to assess differences in mortality as a result of an imaging-based diagnostic method, a randomised trial will require several repeated screens by the same method in each study group, and the strong results from all studies to date will probably result in substantial crossover and self-selection biases over time. Second, because of the high survival rate (or low mortality rate) of breast cancer, the study will require long follow-up times of at least 10 years. In a rapidly changing environment in terms of improvements in screening technologies and therapeutic interventions, the avoidance of biases is likely to be very difficult, if not impossible. The use of the number of interval cancers and possible shifts in stage at detection, while appropriately accounting for confounders, would be almost as daunting a task. Third, the imaging detection of cancer is only the first step in many management decisions and interventions that can affect outcome. The appropriate control of biases related to patient management is highly unlikely. The arguments above, in addition to the existing reports to date that show substantial improvements in cancer detection, particularly with the detection of invasive cancers, with a simultaneous reduction in recall rates, support the argument that a randomised trial is neither necessary nor warranted. The current technology might be obsolete by the time results of an appropriately done and analysed randomised trial is made public.”

In order to better link the information given by “scientific” papers to the context of daily patients’ reality I suggest to spend some time reviewing few of the videos in the below links:

The following group of videos is featured on a website by Siemens. Nevertheless, the presenting radiologists are leading practitioners who affects thousands of lives every year – What the experts say about tomosynthesis. – click on ECR 2013
Breast Tomosynthesis in Practice – part of a commercial ad of the Washington Radiology Associates featured on the website of Diagnostic Imaging. As well, affects thousands of lives in the Washington area every year.

The pivotal questions yet to be answered are:

What should be done in order to translate increase in sensitivity and early detection into decrease in mortality?
What is the price of such increase in sensitivity in terms of quality of life and health-care costs and is it worth-while to pay?

An article that summarises positively the experience of introducing Tomosynthesis into routine screening practice was recently published on AJR:

Implementation of Breast Tomosynthesis in a Routine Screening Practice: An Observational Study

Stephen L. Rose¹, Andra L. Tidwell¹, Louis J. Bujnoch¹, Anne C. Kushwaha¹, Amy S. Nordmann¹ and Russell Sexton, Jr.¹

Affiliation: ¹ All authors: TOPS Comprehensive Breast Center, 17030 Red Oak Dr, Houston, TX 77090.

Citation: American Journal of Roentgenology. 2013;200:1401-1408

ABSTRACT :

OBJECTIVE. Digital mammography combined with tomosynthesis is gaining clinical acceptance, but data are limited that show its impact in the clinical environment. We assessed the changes in performance measures, if any, after the introduction of tomosynthesis systems into our clinical practice.

MATERIALS AND METHODS. In this observational study, we used verified practice- and outcome-related databases to compute and compare recall rates, biopsy rates, cancer detection rates, and positive predictive values for six radiologists who interpreted screening mammography studies without (n = 13,856) and with (n = 9499) the use of tomosynthesis. Two-sided analyses (significance declared at p < 0.05) accounting for reader variability, age of participants, and whether the examination in question was a baseline were performed.

RESULTS. For the group as a whole, the introduction and routine use of tomosynthesis resulted in significant observed changes in recall rates from 8.7% to 5.5% (p < 0.001), nonsignificant changes in biopsy rates from 15.2 to 13.5 per 1000 screenings (p = 0.59), and cancer detection rates from 4.0 to 5.4 per 1000 screenings (p = 0.18). The invasive cancer detection rate increased from 2.8 to 4.3 per 1000 screening examinations (p = 0.07). The positive predictive value for recalls increased from 4.7% to 10.1% (p < 0.001).

CONCLUSION. The introduction of breast tomosynthesis into our practice was associated with a significant reduction in recall rates and a simultaneous increase in breast cancer detection rates.

Here are the facts in tables and pictures from this article

Metabolomics based biomarker discoveries

Posted in Biomarkers & Medical Diagnostics, Biomedical Measurement Science, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Prevention: Research & Programs, Chemical Biology and its relations to Metabolic Disease, Metabolomics, Population Health Management, Genetics & Pharmaceutical, Population Health Management, Nutrition and Phytochemistry, tagged biomarkers, Cancer - General, carcinoma, Cardiovascular disease, Metabolomics, NMR on July 9, 2013| 6 Comments »

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

Molecular biomarkers could detect biochemical changes associated with disease processes. The key metabolites have become an important part for improving the diagnosis, prognosis, and therapy of diseases. Because of the chemical diversity and dynamic concentration range, the analysis of metabolites remains a challenge. Assessment of fluctuations on the levels of endogenous metabolites by advanced NMR spectroscopy technique combined with multivariate statistics, the so-called metabolomics approach, has proved to be exquisitely valuable in human disease diagnosis. Because of its ability to detect a large number of metabolites in intact biological samples with isotope labeling of metabolites using nuclei such as H, C, N, and P, NMR has emerged as one of the most powerful analytical techniques in metabolomics and has dramatically improved the ability to identify low concentration metabolites and trace important metabolic pathways. Multivariate statistical methods or pattern recognition programs have been developed to handle the acquired data and to search for the discriminating features from biosample sets. Furthermore, the combination of NMR with pattern recognition methods has proven highly effective at identifying unknown metabolites that correlate with changes in genotype or phenotype. The research and clinical results achieved through NMR investigations during the first 13 years of the 21st century illustrate areas where this technology can be best translated into clinical practice.

In the last decade, proteomics and metabolomics have contributed substantially to our understanding of cardiovascular diseases. The unbiased assessment of pathophysiological processes without a priori assumptions complements other molecular biology techniques that are currently used in a reductionist approach. A discrete biological function is very rarely attributed to a single molecule; more often it is the combined input of many proteins. In contrast to the reductionist approach, in which molecules are studied individually, “omics” platforms allow the study of more complex interactions in biological systems. Combining proteomics and metabolomics to quantify changes in metabolites and their corresponding enzymes will advance our understanding of pathophysiological mechanisms and aid the identification of novel biomarkers for cardiovascular disease.

Marginal deficiency of vitamin B-6 is common among segments of the population worldwide. Because pyridoxal 5′-phosphate serves as a coenzyme in the metabolism of amino acids, carbohydrates, organic acids, and neurotransmitters, as well as in aspects of one-carbon metabolism, vitamin B-6 deficiency could have many effects. NMR spectral features of selected metabolites indicated that vitamin B-6 restriction significantly increased the ratios of glutamine/glutamate and 2-oxoglutarate/glutamate and tended to increase concentrations of acetate, pyruvate, and trimethylamine-N-oxide. Tandem MS showed significantly greater plasma proline after vitamin B-6 restriction, but there were no effects on the profile of 14 other amino acids and 45 acylcarnitines. These findings demonstrate that marginal vitamin B-6 deficiency has widespread metabolic perturbations and illustrate the utility of metabolomics in evaluating complex effects of altered vitamin B-6 intake.

Hepatocellular carcinoma is one of the most common malignancies worldwide, and it has a poor prognosis due to its rapid development and early metastasis. An understanding of tumor metabolism would be helpful for the clinical diagnosis and therapy of hepatocellular carcinoma. To investigate the metabolic features of hepatocellular carcinoma, a non-targeted metabolic profiling strategy based on liquid chromatography-mass spectrometry was performed. The results revealed multiple metabolic changes in the tumor, and the principal changes included elevated glycolysis, inhibition of the tricarboxylic acid cycle, accelerated gluconeogenesis and β-oxidation for energy supply and down-regulated Δ-12 desaturase. Furthermore, increased levels of anti-oxidative molecules, such as glutathione, and decreased levels of inflammatory-related polyunsaturated fatty acids and the phospholipase A2 enzyme were also observed. The differential metabolites found in the tissue were tested in serum samples from the chronic hepatitis, cirrhosis and hepatocellular carcinoma patients. The combination of betaine and propionylcarnitine was confirmed to have a good diagnostic potential to distinguish hepatocellular carcinoma from chronic hepatitis and cirrhosis. External validation of cirrhosis and hepatocellular carcinoma serum samples further shows the combination biomarker is useful for hepatocellular carcinoma diagnosis.

Current diagnostic techniques have increased the detection of prostate cancer; however, these tools inadequately stratify patients to minimize mortality. Recent studies have identified a biochemical signature of prostate cancer metastasis, including increased sarcosine abundance. Prostate tumors had significantly altered metabolite profiles compared to cancer-free prostate tissues, including biochemicals associated with cell growth, energetics, stress, and loss of prostate-specific biochemistry. Many metabolites were further associated with clinical findings of aggressive disease. Aggressiveness-associated metabolites stratified prostate tumor tissues with high abundances of compounds associated with normal prostate function (e.g., citrate and polyamines) from more clinically advanced prostate tumors. These aggressive prostate tumors were further subdivided by abundance profiles of metabolites including NAD+ and kynurenine. When added to multiparametric nomograms, metabolites improved prediction of organ confinement and 5-year recurrence. These findings support and extend earlier metabolomic studies in prostate cancer and studies where metabolic enzymes have been associated with carcinogenesis and/or outcome. Furthermore, it suggests that panels of analytes may be valuable to translate metabolomic findings to clinically useful diagnostic tests.

Source References:

http://www.ncbi.nlm.nih.gov/pubmed/23828598

http://www.ncbi.nlm.nih.gov/pubmed/23827455

http://www.ncbi.nlm.nih.gov/pubmed/23776431

http://www.ncbi.nlm.nih.gov/pubmed/23824744

http://www.ncbi.nlm.nih.gov/pubmed/23824564

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In focus: Circulating Tumor Cells

Posted in Bio Instrumentation in Experimental Life Sciences Research, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Biomedical Measurement Science, CANCER BIOLOGY & Innovations in Cancer Therapy, FDA Regulatory Affairs, Medical Devices R&D Investment, tagged biomarkers, Blood test, breast cancer, CD44, CD47, CellSearch System, Circulating tumor cell, colon cancer, CTC, EPCAM, esophageal cancer, FDA, immunomagnetic, Key words: circulating tumor cells, Martin Fleisher, metastasis, metastasis initiating cells, MIC, overall survival, progression-free survival, Prostate cancer, Targeted therapy, tumor microenvironment, University of Texas MD Anderson Cancer Center, Veridex LLC, xenograft on June 24, 2013| 4 Comments »

Author/Curator: Ritu Saxena, PhD

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Word Cloud By Danielle Smolyar

For several decades, research efforts have focused on targeting progression of cancer cells in primary tumors. Primary tumor cell targeting strategies include standard chemotherapy and immunotherapy and modulation of host microenvironment including tumor vasculature. However, cancer progression is comprised of both primary tumor growth and secondary metastasis (Langley RR and Fidler IJ. Tumor cell-organ microenvironment interactions in the pathogenesis of cancer metastasis. Endocr Rev. 2007 May;28(3):297-321; http://www.ncbi.nlm.nih.gov/pubmed/17409287). Owing to the property of unilimited cell division, cells in primary tumor increase rapidly in number and density and are able to favorably influence their microenvironment. Metastasis, on the other hand, depends on the ability of cancer cells to disseminate, circulate, adapt to the harsh environment and seed in different organs to establish secondary tumors. Although tumor cells are shed into the circulation in large numbers since early stages of tumor formation, few tumor cells can survive and proceed to overt metastasis. (Husemann Y et al. Systemic spread is an early step in breast cancer. Cancer Cell. 2008 Jan;13(1):58-68; http://www.ncbi.nlm.nih.gov/pubmed/18167340). Tight vascular wall barriers, unfavorable conditions for survival in distant organs, and a rate-limiting acquisition of organ colonization functions are just some of the impediments to the formation of distant metastasis (Chiang AC and Massagué J. Molecular basis of metastasis. N Engl J Med. 2008 Dec 25;359(26):2814-23; http://www.ncbi.nlm.nih.gov/pubmed/19109576).

It has been hypothesized that metastasis is initiated by a subpopulation of circulating tumor cells (CTC) found in the blood of patients. Therefore, understanding the function of CTC and targeting the CTC is gaining attention as a possible therapeutic avenue in carcinoma treatment.

Figure: Circulating tumor cells in the metastatic cascade

(Image source: Chaffer CL and Weinberg RA. Science 2011,331, pp. 1559-1564; http://www.ncbi.nlm.nih.gov/pubmed/21436443)

Isolation of CTC

Initial methods relied on the difference in physical properties of cells. When spun in a centrifuge, different cells in the blood sample settle in separate layers based on their byoyancy, and CTC are found in the white blood cell fraction. Because CTC are generally larger than white blood cells, a size-based filter could be used to separate the cell types (Vona G, et al, Isolation by size of epithelial tumor cells : a new method for the immunomorphological and molecular characterization of circulating tumor cells. Am J Pathol, 2000 Jan;156(1):57-63; http://www.ncbi.nlm.nih.gov/pubmed/10623654).

Herbert A Fritsche, PhD, Professor and Chief, Clinical Chemistry, Department of Laboratory Medicine, The University of Texas, MD Anderson Cancer Center, demonstrated that the CTC can be captured using antibody labeled magnetic beads, either in positive or negative selection schema. After the circulating tumor cells are isolated, they may be characterized by immunohistochemistry and counted. Alternatively, these cells may be characterized by gene expression analysis using RT-PCR. One of the CTC detection methods, Veridex Inc, Cell Search Assay, has been cleared by the US FDA for use as a prognostic test in patients with metastatic cancers of the breast, prostate and colon. This technology relies on the expression of epithelial cellular adhesion molecular (EpCAM) by epithelial cells and the isolation of these cells by immunomagnetic capture using anti-EpCAM antibodies. Enriched CTC are identified by immunofluorescence. Martin Fleisher, PhD, Chair, Department of Clinical Laboratories, Memorial Sloan-Kettering Cancer Center discussed in a webinar at the biomarker symposia, Cambridge Healthtech Institute, that every new technology has shortcomings, and the reliance on cancer cells to express sufficient EpCAM to enable capture may affect the role of this technology in future clinical use. Heterogeneous downregulation of epithelial surface antigen in invasive tumor cells has been reported. Thus, alternative methods to detect CTC are being developed. These new methods include-

Flow cytometry that sorts cells by size and surface antigen expression.
CTC microchips that are designed to capture CTC as whole blood flows past EpCAM-coated mirco-posts.
Enrichment by filtration using filters with a pore size of 7-8 µm, that permits smaller red blood cell, leukocytes, and platelets to pass, but captures CTC that have diameters of about 12-15 µm.

Better identification of CTC

Baccelli et al (2013) developed a xenograft assay and demonstrated that the primary human luminal breast cancer CTC contain metastasis-initiated cells (MICs) that give rise to bone, lung and liver metastases in mice. These MIC-containing CTC populations expressed EPCAM, CD44, CD47 and MET. It was observed that in a small cohort of patients with metastases, the number of CTC expressing markers EPCAM,CD44, CD47 and MET, but not of bulk EPCAM+ CTC, correlated with lower overall survival and increased number of metastasic sites. These data describe functional circulating MICs and associated markers, which may aid the design of better tools to diagnose and treat metastatic breast cancer. The findings were published in the Nature Biotechnology journal recently (Baccelli I, et al. Identification of a population of blood circulating tumor cells from breast cancer patients that initiates metastasis in a xenograft assay. Nature Biotechnology 2013 31, 539–544; http://www.ncbi.nlm.nih.gov/pubmed/23609047).

CTC as prognostic and predictive factor for cancer progression

Martin Fleisher, PhD states “detecting CTC in peripheral blood of patients with cancer has become a clinically relevant and important prognostic biomarker and has been shown to be a predictive biomarker post-therapy. But, key to the use of CTC as a biomarker is the technology designed to enrich cancer cells from peripheral blood.”

Since CTC isolation methods started being established, correlation studies between the cells and a patient’s disease emerged. In 2004, investigators at the Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center (Houston, TX) discovered that the CTC were associated with disease progression and survival in metastatic breast cancer. The clinical trial recruited 177 patients with measurable metastatic breast cancer for levels of CTC both before the patients were to start a new line of treatment and at the first follow-up visit. The progression of the disease or the response to treatment was determined with the use of standard imaging studies at the participating centers. Patients in a training set with levels of CTC equal to or higher than 5 per 7.5 ml of whole blood, as compared with the group with fewer than 5 CTC per 7.5 ml, had a shorter median progression-free survival (2.7 months vs. 7.0 months, P<0.001) and shorter overall survival (10.1 months vs. >18 months, P<0.001). At the first follow-up visit after the initiation of therapy, this difference between the groups persisted (progression-free survival, 2.1 months vs. 7.0 months; P<0.001; overall survival, 8.2 months vs. >18 months; P<0.001), and the reduced proportion of patients (from 49 percent to 30 percent) in the group with an unfavorable prognosis suggested that there was a benefit from therapy. Thus, the number of CTC was found to be an independent predictor of progression-free survival and overall survival in patients with metastatic breast cancer (Cristofanilli M, et al, Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med. 2004 Aug 19;351(8):781-91; http://www.ncbi.nlm.nih.gov/pubmed/15317891).

Similar results have been observed in other cancer types, including prostate and colorectal cancer. The Cell Search System developed by Veridex LLC (Huntingdon Valley, PA) enumerated CTC from 7.5 mL of venous blood and was used to compare the outcomes from three prospective multicenter studies investigating the use of CTC to monitor patients undergoing treatment for metastatic breast, colorectal, or prostate cancer. Evaluation of CTC at anytime during the course of disease allowed assessment of patient prognosis and is predictive of overall survival (Miller MC, et al. Significance of Circulating Tumor Cells Detected by the CellSearch System in Patients with Metastatic Breast Colorectal and Prostate Cancer. J Oncol. 2010; http://www.ncbi.nlm.nih.gov/pubmed/20016752). In addition, the CTC test may permit the oncologist to make an early decision to discontinue first line therapy for metastatic breast cancer and pursue more aggressive alternative treatments.

Genetic analysis of CTC

Additional studies have analyzed the genetic mutations that the cells carry, comparing the mutations to those in a primary tumor or correlating the findings to a patient’s disease severity or spread. In one study, lung cancer patients whose CTC carried a mutation known to cause drug resistance had faster disease progression than those whose CTC lacked the mutation. The investigators analyzed the evolutionary aspect of cancer progression and studied the precursor cells of metastases directly for the identification of prognostic and therapeutic markers. Single disseminated cancer cells isolated from lymph nodes and bone marrow of 107 consecutive esophageal cancer patients were analyzed by whole-genome screening which revealed that primary tumors and lymphatically and hematogenously disseminated cancer cells diverged for most genetic aberrations. Chromosome 17q12-21, the region comprising HER2, was identified as the most frequent gain in disseminated tumor cells that were isolated from both ectopic sites. Furthermore, survival analysis demonstrated that HER2 gain in a single disseminated tumor cell but not in primary tumors conferred high risk for early death (Stoecklein NH, et al. Direct genetic analysis of single disseminated cancer cells for prediction of outcome and therapy selection in esophageal cancer. Cancer Cell. 2008 May;13(5):441-53; http://www.ncbi.nlm.nih.gov/pubmed/18455127).

The abovementioned studies indicate that CTC blood tests have been successfully used to track the severity of a cancer or efficacy of a treatment. In conclusion, the evolution of the CTC technology will be critical in the emerging area of targeted therapy. With the development and use of new technologies, the links between the genomic information and CTC could be explored and established for targeted therapy.

Challenges in CTC research

Potential clinical significance of CTC has been demonstrated as early detection, diagnostic, prognostic, predictive, surrogate, stratification, and pharmacodynamic biomarkers. Hong B and Zu Y (2013) discuss that “the role of CTC as a disease marker may be unique in different clinical conditions and should be carefully interpreted. A good example is the comparison between the prognostic and predictive biomarkers. Both biomarkers employ progression-free survival and overall survival for data interpretation; however, the prognostic biomarker is independent of specific drug treatment or therapy, and used for the determination of outcomes before treatment, while the predictive biomarker is related to a particular treatment to predict the response. Furthermore, inconsistent results are increasingly reported among the various CTC assay methods, specifically pertaining to results for the CTC detection rate, patient positivity rate, and the correlation between the presence of CTC and survival rate (Hong B and Zu Y. Detecting circulating tumor cells: current challenges and new trends. Source. Theranostics. 2013 Apr 23;3(6):377-94; http://www.ncbi.nlm.nih.gov/pubmed/23781285).
Heterogeneity in CTC along with several other technical factors contribute to discordance, including the changes in methodology, lack of reference standard, spectrum and selection bias, operator variability and bias, sample size, blurred clinical impact with known clinical/pathologic data, use of diverse capture antibodies from different sources, lack of awareness of the pre-analytical phase, oversimplification of the cytopathology process, use of dichotomous decision criteria, etc (Sturgeon C. Limitations of assay techniques for tumor markers. In: (ed.) Diamandis EP, Fritsche HA, Lilja H, Chan DW, Schwartz MK. Tumor markers: physiology, pathobiology, technology, and clinical applications. Washington, DC: AACC Press. 2002:65-82; Gion M and Daidone MG. Circulating biomarkers from tumour bulk to tumour machinery: promises and pitfalls. Eur J Cancer. 2004;40(17):2613-2622; http://www.ncbi.nlm.nih.gov/pubmed/15541962). Therefore, employing a standard protocol is essential in order to minimize a lot of inconsistencies and technical errors.
CTC in a small amount of blood sample might not represent the actual CTC count in the whole blood. In fact, it has been reported that the Cell Search system might undercount the number of CTC. Nagrath et al (2007) have demonstrated that the average CTC number per mL of whole blood is approximately 79-155 in various cancers (Nagrath S, et al. Isolation of rare circulating tumous cells in cancer patients by microchip technology. Nature. 2007;450(7173):1235-1239; http://www.ncbi.nlm.nih.gov/pubmed/18097410). In addition, an investigative CellSearch Profile approach (for research use only) detected an approximately 30-fold higher number of the median CTC in the same paired blood samples (Flores LM, et al. Improving the yield of circulating tumour cells facilitates molecular characterisation and recognition of discordant HER2 amplification in breast cancer. Br J Cancer. 2010;102(10):1495-502; http://www.ncbi.nlm.nih.gov/pubmed/20461092). Such measurement discrepancies indicate that the actual CTC numbers in the blood of patients could be at least 30-100 fold higher than that currently reported by the only FDA-cleared CellSearch system.

Thus, although promising, the CTC technology faces several challenges both in detection and interpretation, which has resulted in its limited clinical acceptance and use. In order to prepare the CTC technology for future widespread clinical acceptance, a comprehensive guideline for all phases of CTC technology development was published by the Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium. The guidelines describe methods for interactive comparisons of proprietary new technologies, clinical trial designs, a clinical validation qualification strategy, and an approach for effectively carrying out this work through a public-private partnership that includes test developers, drug developers, clinical trialists, the FDA and the National Cancer Institute (NCI) (Parkinson DR, et al. Considerations in the development of circulating tumor cell technology for clinical use. J Transl Med. 2012;10(1):138; http://www.ncbi.nlm.nih.gov/pubmed/22747748).

Reference:

Langley RR and Fidler IJ. Tumor cell-organ microenvironment interactions in the pathogenesis of cancer metastasis. Endocr Rev. 2007 May;28(3):297-321; http://www.ncbi.nlm.nih.gov/pubmed/17409287
Husemann Y et al. Systemic spread is an early step in breast cancer. Cancer Cell. 2008 Jan;13(1):58-68; http://www.ncbi.nlm.nih.gov/pubmed/18167340
Chiang AC and Massagué J. Molecular basis of metastasis. N Engl J Med. 2008 Dec 25;359(26):2814-23; http://www.ncbi.nlm.nih.gov/pubmed/19109576
Vona G, et al, Isolation by size of epithelial tumor cells : a new method for the immunomorphological and molecular characterization of circulating tumor cells. Am J Pathol, 2000 Jan;156(1):57-63; http://www.ncbi.nlm.nih.gov/pubmed/10623654
Baccelli I, et al. Identification of a population of blood circulating tumor cells from breast cancer patients that initiates metastasis in a xenograft assay. Nature Biotechnology 2013 31, 539–544; http://www.ncbi.nlm.nih.gov/pubmed/23609047
Cristofanilli M, et al, Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med. 2004 Aug 19;351(8):781-91; http://www.ncbi.nlm.nih.gov/pubmed/15317891
Miller MC, et al. Significance of Circulating Tumor Cells Detected by the CellSearch System in Patients with Metastatic Breast Colorectal and Prostate Cancer. J Oncol. 2010; http://www.ncbi.nlm.nih.gov/pubmed/20016752
Stoecklein NH, et al. Direct genetic analysis of single disseminated cancer cells for prediction of outcome and therapy selection in esophageal cancer. Cancer Cell. 2008 May;13(5):441-53; http://www.ncbi.nlm.nih.gov/pubmed/18455127
Hong B and Zu Y. Detecting circulating tumor cells: current challenges and new trends. Source. Theranostics. 2013 Apr 23;3(6):377-94; http://www.ncbi.nlm.nih.gov/pubmed/23781285
10. Sturgeon C. Limitations of assay techniques for tumor markers. In: (ed.) Diamandis EP, Fritsche HA, Lilja H, Chan DW, Schwartz MK. Tumor markers: physiology, pathobiology, technology, and clinical applications. Washington, DC: AACC Press. 2002:65-82
Gion M and Daidone MG. Circulating biomarkers from tumour bulk to tumour machinery: promises and pitfalls. Eur J Cancer. 2004;40(17):2613-2622; http://www.ncbi.nlm.nih.gov/pubmed/15541962
Nagrath S, et al. Isolation of rare circulating tumous cells in cancer patients by microchip technology. Nature. 2007;450(7173):1235-1239; http://www.ncbi.nlm.nih.gov/pubmed/18097410
Flores LM, et al. Improving the yield of circulating tumour cells facilitates molecular characterisation and recognition of discordant HER2 amplification in breast cancer. Br J Cancer. 2010;102(10):1495-502; http://www.ncbi.nlm.nih.gov/pubmed/20461092
Chaffer CL and Weinberg RA. Science 2011,331, pp. 1559-1564; http://www.ncbi.nlm.nih.gov/pubmed/21436443

Modulating Stem Cells with Unread Genome: microRNAs

Posted in Biological Networks, Gene Regulation and Evolution, Genome Biology, Personalized and Precision Medicine & Genomic Research, Pharmaceutical R&D Investment, Pharmacogenomics, Stem Cells for Regenerative Medicine, tagged biomarkers, Cell Biology, Dicer, Doctor of Philosophy, Drosophila, homeotic genes, Messenger RNA, MicroRNA, polycomb complexes, Regulation of gene expression, Stem cell on March 31, 2013| 2 Comments »

Modulating Stem Cells with Unread Genome: microRNAs

Author, Demet Sag, PhD

Life is simple but complicated. Both simple specific sequences and the big picture approach as a system are necessary in applications for a coherent outcome. Thus, providing an engineered whole cell as a system of correction for “Stem Cell Therapy” may resolve unmet health problems. Only 1% of the genome is read and the remaining 99% is not a junk but useful. The energy is never getting lost and there is a tight conservation economy in living organisms. As an example microRNAs that are one of the families of untranslated sequences can be utilized for a stem cell therapy for cancer. Their power lies at transcription control that may direct the cell expression at exact time, and place for diagnosing, imaging and treatment. The development of cell biology and understanding of genetic data from model organisms will assist to design a well-working mechanism.

In 1964, after their elegant experiment Till et. al demonstrated that special stimulating factors caused the differentiation and made new colonies. They suggested that “…since stem cells are responsible for continued cell production, it would appear probable that such stem cells are the sites of action for control mechanisms.” They also pointed out simply that some cells do continue to be stem cells and some do loose the plasticity as they differentiate. Regardless of the two major unescapable events, “the birth” and “the death”, even though can be less predictable than the other, life must go on. This nature brought an attention to regenerate the cells for our need.

One of the main issues in stem cell biology is figuring out how to re-activate once upon a time fast dividing cells, while the rest of the cells were not even active. The short answer is escaping the control gates with the precise keys without creating any immune responses or toxicity. The easiest and safest method is to re-write instructions of the cells for making a function based on comparative system biology and development. These retrained, resensitized and reprogrammed cells make possible changes to produce right amount of protein(s) on time and its place.

Functional genomics approach to a system within conserved life mechanisms of organisms (C elegans, D. melanogaster, A. nidulans, S. cerevisiae and M. musculus) is necessary for sound principles development.

The first resolution comes from the worm, C. elegans. The early founding fathers of these special 20-22 bp untranslated specific sequences that control time in development and possible mRNA regulation are called microRNAs. This significant signature sequences and biomarkers control gene regulation for a proper protein expression even though these whistles and bells are not even expressed. Since they are included in 99% of the genome, they must have a voice in the system. These miRNAs are shown first time in C.elegans were lin4 and let7. When they were mutated, the cells went onto extra cell proliferation like it would in cancer. Later, in many metazoans it was discovered and shown that these special RNAs negatively regulate specific gene expression during important developmental stages of life such as cell proliferation, apoptosis and stress response. For example the famous Drosha and Dicer, members of the RNA H III family, is acting sequentially in Drosophila bind to un-translated region of mRNA that either preventing the expression of the protein or causing to be degraded by RISC (He and Hannon 2004).

Dicer is important in biogenesis of miRNA pathway and Drosophila ovary is a great tool to study embryonic stem cells. Analysis of Dicer-1 (dcr-1) germline mutants showed that these mutants have fewer cysts because at G1/S checkpoint the activity of Decapo, a cyclin kinase inhibitor, depends on Dicer-1. As a result, cell division mechanisms require functional miRNA. In addition, these miRNAs also make the cells “insensitive” to the environmental influences. The new epigenetic studies include their function for oncology RD to increase efficacy and survival rate of the treatment along with personalized genomic data.

The new technologies screening of the genome or doing chromosome walk became less labor intense and more informative like miccroarray technology, faster sequencing. Lu’s group designed a microarray analysis on comparative differential expression of miRNAs between healthy and tumor in human. Their data show that there is a difference between these populations besides having specific loci for miRNAs in the genome (Lu et al. 2005). The study by O’Dennel’s group reaffirmed their finding. Microarray screening showed several miRNAs are residing at the chromosome 13 region. These miRNAs are also interacting specifically with MYC to modulate the cell genesis during cancer development (O’Dennel et al. 2005).

Yet, recent evidences show that miRNAs also manipulate regulation of transcription and epigenetics (Wang et. al 2013). As a result, nanomolecules without affecting the cellular life with specific miRNAs help us to imagine of this complexity and to receive the snapshot of the condition (Conde et al. 2013).

Furthermore, there is a complexity to be included in the design of molecules. The system mechanism may bring solutions for human health. Thus, modulated stem cells with engineered special future based on not only one gene-one enzyme theory but also many/one gene, one/many enzyme. For example, Schwartz group showed that polycomb group of genes made up of several hundred genes manipulate a complete function in the system of organism (Schwartz et al. 2007). First polycombs were found in fruit flies (Drosophila), but they are recognized that they function to regulate homeotic genes both in mammals and insects. Now, it is known that these polycomb complexes play a huge global role in organizing epigenetics by enforcing repressed states, but balanced by Trithorax. Interestingly, even same genes function in both germline and somatic sex determination pathway, there are different cell-cell communications, signal transductions and players in regulation mechanisms of Drosophila (Salz 2013; Ng et al. 2013).

Therefore, the studies modulating cells by engineering oligos may fix a health problem. Immunomodulation of immune cells APC (antigen presenting cells) / DC (dentritic cells) / T (T/B), reprogramming stem cells and restructuring of the membrane receptors for increased sensitivity to protect/locate/activate are few examples of possible platforms to develop products.

Life is simple but complex, also there is a simple solution, since human is the most resilient living who will answer how to cure what is broken to survive.

References:

A Stochastic model of stem cell proliferation,based on th egrowth of spleen xcolony-forming cells. J. E. Till, E. A. McCulloch, L. Siminovitch Proc Natl Acad Sci U S A. 1964 January; 51(1): 29–36. PMCID: PMC300599. (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC300599/)
MicroRNAs: Small RNAs with a big role in gene regulation L. He, G.J. Hannon Nat. Rev. Genet., 5 (2004), pp. 522–531 (http://www.nature.com/nrg/journal/v5/n7/full/nrg1379.html)
Stem cell division is regulated by the microRNA pathway. S.D. Hatfield, H.R. Shcherbata, K.A. Fischer, K. Nakahara, R.W. Carthew, H. Ruohola-Baker Nature, 435 (2005), pp. 974–978 (http://www.nature.com/nature/journal/v435/n7044/full/nature03816.html)
MicroRNA expression profiles classify human cancers. J. Lu, G. Getz, E.A. Miska, E. Alvarez-Saavedra, J. Lamb, D. Peck, A. Sweet-Cordero, B.L. Ebert, R.H. Mak, A.A. Ferrando et al. Nature, 435 (2005), pp. 834–838 (http://www.nature.com/nature/journal/v435/n7043/full/nature03702.html)
c-Myc-regulated microRNAs modulate E2F1 expression. K.A. O’Donnell, E.A. Wentzel, K.I. Zeller, C.V. Dang, J.T. Mendell Nature, 435 (2005), pp. 839–843 (http://www.nature.com/nature/journal/v435/n7043/full/nature03677.html)
Gold-nanobeacons for simultaneous gene specific silencing and intracellular tracking of the silencing events. J. Conde, J, Rosa, J. M. la Fuente, P. V. Baptista. Biomaterials, Vol. 34, issue 10, March 2013, pp. 2516-2523 (http://www.sciencedirect.com/science/article/pii/S0142961212013956)
Transcriptional and epigenetic regulation of human microRNAs.
Zifeng Wang,Hong Yao, Sheng Lin, Xiao Zhu, Zan Shen, Gang Lu, Wai Sang Poon, Dan Xie, Marie Chia-mi Lin, Hsiang-fu Kung. Cancer Letters Volume 331, Issue 1 , Pages 1-10, 30 April 2013. (http://www.cancerletters.info/article/S0304-3835(12)00723-9/abstract)
The MSC: An Injury Drugstore. A. I. Caplan and D. Correa Cell Stem Cell. 2011 July 8; 9(1): 11–15. doi:10.1016/j.stem.2011.06.008.
Polycomb silencing mechanisms and the management of genomic programmes. Schwartz YB, Pirrotta V (January 2007). Nat. Rev. Genet. 8 (1): 9–22. doi:10.1038/nrg1981. PMID 17173055. (http://www.ncbi.nlm.nih.gov/pubmed/17173055)
Sex, stem cells and tumors in the Drosophila ovary. HK Salz, Fly, 2013 (http://www.landesbioscience.com/journals/fly/article/22687/)
In Vivo Epigenomic Profiling of Germ Cells Reveals Germ Cell Molecular Signatures. J. Ng, V. Kumar, M. Muratani, P. Kraus, JC. Yeo, L-P. Yaw, K. XUe, T. Lufkin, S. Prabhakar, H-H, Ng. Developmental Cell, Vol. 24, Issue 3, 11 February 2013, Pages 324–333. (http://www.sciencedirect.com/science/article/pii/S1534580712005850)

In Focus: Targeting of Cancer Stem Cells

Posted in Bio Instrumentation in Experimental Life Sciences Research, CANCER BIOLOGY & Innovations in Cancer Therapy, Pharmacotherapy and Cell Activity, Stem Cells for Regenerative Medicine, tagged ABC, ABCC1, ABCG2, anti-cancer therapy, antiapoptosis, Bcl-2, biomarkers, cancer stem cells, CD133, chemoresistance, DNA damage, DNA repair, efflux transporter, glioblastoma multiforme, Glioma, Imatinib, Interleukin 6, LSCs, MDR1, Multi-drug resistance, Non-small cell lung cancer, radiotherapy resistance, self-renewal, stem cells, TGFβ, tumors, Wnt/β catenin, xeno transplantation model on March 27, 2013| 9 Comments »

Author and Curator: Ritu Saxena, Ph.D

Although cancer stem cells constitute only a small percentage of the tumor burden, their self-renewal capacity and possible link with recurrence of cancer post treatment makes them a sought after therapeutic target in cancer. The post on cancer stem cells published on the 22^nd of March, 2013, describes the identity of CSCs, their functional characteristics, possible cell of origin and biomarkers. This post focuses on the therapeutic potential of CSCs, their resistance to conventional anti-tumor therapies and current therapeutic targets including biomarkers, signaling pathways and niches.

CSCs Are Resistant to conventional anticancer therapies including chemotherapy, radiotherapy and surgery that are used either alone or in combination. However, these strategies have failed several times to eradicate CSCs resulting in metastasis and relapse, hence, a fatal disease outcome.

The properties of CSCs that contribute to or lead to chemoresistance include:

Quiescent Phenotype

Chemotherapeutic agents target fast-growing cells; however, some CSCs that remain in the dormant or quiescent stage are spared from lethal damage. Later, when the dormant CSCs enter cell cycle, tumor proliferation is stimulated.

Antiapoptosis

Antiapoptotic proteins such as BCL-2 and some self-renewal pathways such as transforming growth factor β, Wnt/ β -catenin or BMI-1 are activated in CSCs. Consequently, DNA damage repair capability of CSCs is enhanced after genotoxic stress or activation of autocrine loops through the production of growth factors like epidermal growth factor (Moserle L, Cancer Lett, 1 Feb 2010;288(1):1-9).

Expression of Drug Efflux Pumps

CSCs express some proteins that have typically been known to contribute to multidrug resistance. The proteins are drug efflux pumps ABCC1, ABCG2 or MDR1. Multidrug resistance-associated proteins (ABCC subfamily) are members of the ATP-binding cassette (ABC) superfamily of transport proteins and act as cellular efflux transporters for a wide variety of substrates, in particular glutathione, glucuronide and sulfate conjugates of diverse compounds.

Radiotherapy is mainly used in breast cancer and glioblastoma multiforme. In glioblastoma multiforme, the properties of CSCs that contribute to radiotherapy resistance is the presence of CD133 marker. CD133+ CSCs preferentially activate DNA damage repair pathway and significantly induced checkpoint kinases that leads to reduced apoptosis in CSCs compared to the CD133- tumor cells (Bao S, Nature, 7 Dec 2006;444(7120):756-60).

Radiotherapy resistance in breast cancer is due to reduced levels of reactive oxygen species in CSCs. In addition, radiation resistance of progenitor cells in an immortalized breast cancer cell line was mediated by the Wnt/β catenin pathway proteins (Diehn M, et al, Nature, 9 Apr 2009;458(7239):780-3; Chen MS, et al, J Cell Sci, 1 Feb 2007;120(Pt 3):468-77).

As mentioned in the previous post on CSCs, CSC targeting therapy could either eliminate CSCs by either killing them after differentiating them from other tumor population, and/or by disrupting their niche. Efficient eradication of CSCs may require the combined ablation of CSCs themselves and their niches. Thus, identification of appropriate and specific markers of CSCs is crucial for targeting them and preventing tumor relapse. Table 1 (adapted from a review article on CSCs by Zhao et al) describes the currently used biomarkers for CSC-targeted therapy (Zhao L, et al, Eur Surg Res, 2012;49(1):8-15).

Table 1

Specific Target	Cancer type	Marker properties and therapy
*Targeting cell markers*
CD24+CD44+ESA+	Pancreatic cancer	Pancreatic CSCs, elevated during tumorigenesis
CD44+CD24–ESA+	Breast cancer	Breast CSCs
EpCAM high CD44+CD166+	Colorectal cancer
CD34+CD38–	AML	broad use as a target for chemotherapy
CD133+	Prostate cancer and breast cancer	5-transmembrane domain cell surface glycoprotein,also a marker for neuron epithelial, hematopoietic and endothelialprogenitor cells
Stro1+CD105+CD44+	Bone sarcoma
Nodal/activin		Knockdown or pharmacological inhibition of its receptorAlk4/7 abrogated self-renewal capacity and in vivo tumorigenicity of CSCs.
*Targeting signaling pathways*
Hedgehog signaling	Upregulated in several cancer types	inhibitors: GDC-0449,PF04449913, BMS-833923, IPI-926 and TAK-441
Wnt/β-catenin signaling	CML, squamous cell carcinoma	Be required for CSC self-renewal and tumor growthinhibitors: PRI-724, WIF-1 and telomerase
Notch signaling	Several cancer types	An important regulator in normal development, adult stem cell maintenance,and tumorigenesis in multiple organs,inhibitors: RO4929097, BMS-906024, IPI-926 and MK0752
PI3K/Akt/PTEN/mTOR,	Several cancer types	The pathway is deregulated in many tumors and used to preferentially target CSCsinhibitors: temsirolimus, everolimus FDA-approved therapy for renal cell carcinoma
*Targeting CSC Niche*
Angiogenesis Niche	Colon cancer, breast cancer, NSCLC	Inhibitor: bevacizumab results in a disruption of the CSC niche, depleted vasculature and a dramatic reduction in the number of CSCs.
Hypoxia (HIF pathway)	Ovarian cancer, lung cancer, cervical cancer	Inhibitors: topotecan and digoxin have been approved for ovarian, lung and cervical cancer
Targeting Micro RNA
miR-200 family		Inhibits EMT and cancer cell migration by direct targeting of E-cadherin transcriptional repressors ZEB1 and ZEB2
Let-7 family		Regulates BT-IC stem cell-like properties by silencing more than one target
miR-124		Related to neuronal differentiation, targets laminin γ1 and integrin β1.
miR-21		Suppresses the self-renewal of embryonic stem cells

The challenge is to develop an effective treatment regimen that prevents survival, self-renewal and differentiation of CSCs and also disturbs their niche without damaging normal stem cells. In order to evaluate the efficiency of CSC-targeting therapies, in vitro models and mouse xenotransplantation models have been used for preclinical studies. Some potential CSC targeting agents in preclinical stages include notch inhibitors for glioblastoma stem cells and telomerase peptide vaccination after chemoradiotherapy of non-small cell lung cancer stem cells Stem Cells (Hovinga KE, et al, Jun 2010;28(6):1019-29; Serrano D, Mol Cancer, 9 Aug 2011;10:96). In addition, several phase II and phase III trials are currently underway to test CSC-targeting drugs focusing on efficacy and safety of treatment.

Reference:

Bao S, Nature, 7 Dec 2006;444(7120):756-60).

Diehn M, et al, Nature, 9 Apr 2009;458(7239):780-3

Chen MS, et al, J Cell Sci, 1 Feb 2007;120(Pt 3):468-77

Zhao L, et al, Eur Surg Res, 2012;49(1):8-15

Hovinga KE, et al, Jun 2010;28(6):1019-29

Serrano D, Mol Cancer, 9 Aug 2011;10:96

Pharmaceutical Intelligence posts:

http://pharmaceuticalintelligence.com/2013/03/22/in-focus-identity-of-cancer-stem-cells/ Author and curator: Ritu Saxena, PhD

http://pharmaceuticalintelligence.com/2012/08/15/to-die-or-not-to-die-time-and-order-of-combination-drugs-for-triple-negative-breast-cancer-cells-a-systems-level-analysis/ Authors: Anamika Sarkar, PhD and Ritu Saxena, PhD

http://pharmaceuticalintelligence.com/2013/03/07/the-importance-of-cancer-prevention-programs-new-perceptions-for-fighting-cancer/ Author: Ziv Raviv, PhD

http://pharmaceuticalintelligence.com/2013/03/03/treatment-for-metastatic-her2-breast-cancer/ Reporter: Larry H Bernstein, MD

http://pharmaceuticalintelligence.com/2013/03/02/recurrence-risk-for-breast-cancer/ Larry H Bernstein, MD

http://pharmaceuticalintelligence.com/2013/02/14/prostate-cancer-androgen-driven-pathomechanism-in-early-onset-forms-of-the-disease/ Curator: Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2013/01/15/exploring-the-role-of-vitamin-c-in-cancer-therapy/ Curator: Ritu Saxena, PhD

http://pharmaceuticalintelligence.com/2013/01/12/harnessing-personalized-medicine-for-cancer-management-prospects-of-prevention-and-cure-opinions-of-cancer-scientific-leaders-httppharmaceuticalintelligence-com/ Curator: Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2013/01/10/the-molecular-pathology-of-breast-cancer-progression/ Author and reporter: Tilda Barliya PhD

http://pharmaceuticalintelligence.com/2012/11/30/histone-deacetylase-inhibitors-induce-epithelial-to-mesenchymal-transition-in-prostate-cancer-cells/ Reporter and Curator: Stephen J. Williams, PhD

http://pharmaceuticalintelligence.com/2012/10/22/blood-vessel-generating-stem-cells-discovered/ Reporter: Ritu Saxena, PhD

http://pharmaceuticalintelligence.com/2012/10/17/stomach-cancer-subtypes-methylation-based-identified-by-singapore-led-team/ Reporter: Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2012/09/17/natural-agents-for-prostate-cancer-bone-metastasis-treatment/ Reporter: Ritu Saxena, PhD

http://pharmaceuticalintelligence.com/2012/08/28/cardiovascular-outcomes-function-of-circulating-endothelial-progenitor-cells-cepcs-exploring-pharmaco-therapy-targeted-at-endogenous-augmentation-of-cepcs/ Aviva Lev-Ari, PhD, RN

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In Focus: Identity of Cancer Stem Cells

Posted in Biomarkers & Medical Diagnostics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Signaling & Cell Circuits, Genome Biology, Stem Cells for Regenerative Medicine, tagged ALDH, anti-cancer therapy, biomarkers, breast cancer, Cancer - General, cancer stem cells, CD34, CD38, CD44, CD96, CML, CSC, CSCs, EMT, Epithelial-mesenchymal transition, genetic heterogeneity, Glioma, Hematopoietic stem cells, hierarchy, HSCs, IL-6, Imatinib, Interleukin 6, LSCs, polarized epithelial cells, self-renewal, stem cells, tumors, tyrosin kinase inhibitor on March 22, 2013| 9 Comments »

Author and Curator: Ritu Saxena, PhD

Screen Shot 2021-07-19 at 6.28.21 PM

Word Cloud By Danielle Smolyar

What are cancer stem cells?

Cancer is a debilitating disease estimated to be responsible for about 7.6 million deaths in 2008 (Jemal A, et al, CA Cancer J Clin, Mar-Apr 2011;61(2):69-90). Thus, extensive research is underway to deal with the various types of cancer. The concept of cancer stem cells (CSC) has surfaced in in the past decade after identification and characterization of CSC-enriched populations in several different types of cancer (Lapidot T, et al, Nature, 17 Feb 1994;367(6464):645-8; Reya T, et al, Nature, 1 Nov 2001;414(6859):105-11; Trumpp A and Wiestler OD, et al, Nat Clin Pract Oncol, Jun 2008;5(6):337-47). Although there has been lot of debate on the cell of origin of CSC, according to the classical concept CSC are defined by their functional properties.

Functional properties of CSC

CSCs are at the top of tumor hierarchy. Regenerative tissues follow a hierarchical organization with adult stem cells at the top maintaining tissues and normal adult cells during homeostasis and regeneration during cell loss from injury. Similarly, several tumors follow the hierarchy with CSC at the top. Hierarchical organization has been reported in several cancer types including but not limited to breast cancer, brain cancer, colon cancer, leukemia and pancreatic cancer (Lapidot T, et al, Nature, 17 Feb 1994;367(6464):645-8; Al-Hajj M, et al, PNAS USA, 1 Apr 200;100(7):3983-8; Singh SK, et al, Nature, 18 Nov 2004;432(7015):396-401; Dalerba P, et al, PNAS USA, 12 Jun 2007;104(24):10158-63; Hermann PC, et al, Cell Stem Cell, 13 Sep 2007;1(3):313-23).
CSCs possess unlimited self-renewal capacity similar to that of physiological stem cells and unlike other differentiated cell types within the tumor. Cancer stem cells can also generate non-CSC progeny that is comprised of differentiated cells and forms tumor bulk.
Some CSs exhibit quiescent or dormant stage. Although not observed in all CSC types, some CSCs have been found to shuttle between quiescent, slow-cycling, and active states. The CSCs in their dormant and slow-cycling stage are less likely to be affected by conventional anti-tumor therapies which generally target rapidly dividing cells. Dormant stage is exhibited even in adult stem cells and the dormant normal stem cells can regain cell division potential during tissue injury (Wilson A, et al, Cell, 12 Dec 2008;135(6):1118-29). Thus, it has been speculated that dormant CSC might be a reason for tumor relapse even after pathologic complete response is observed post therapy.
Some CSCs are resistant to conventional anti-cancer therapies. This leads to accumulation of CSC that might result in relapse after anti-cancer therapy. For instance, Li et al (2008) reported that CSC accumulated in the breast of women with locally advanced tumors after cytotoxic chemotherapy had eliminated the bulk of the tumor cells (Li X,et al, J Natl Cancer Inst, 7 May 2008;100(9):672-9). A similar observation was made by Oravecz-Wilson et al (2009) stating that despite remarkable responses to the tyrosine kinase inhibitor imatinib, CML patients show imatinib refractoriness because leukemia stem cells in CML are resistant tyrosine kinase (Oravecz-Wilson KI, et al, Cancer Cell, 4 Aug 2009;16(2):137-48).
The CSC niche. CSC functional traits might be sustained by this microenvironment, termed “niche”. The niche is the environment in which stem cells reside and is responsible for the maintenance of unique stem cell properties such as self-renewal and an undifferentiated state. The heterogeneous populations which constitute a niche include both stem cells and surrounding differentiated cells. The necessary intrinsic pathways that are utilized by this cancer stem cell population to maintain both self-renewal and the ability to differentiate are believed to be a result of the environment where cancer stem cells reside. (Cabarcas SM, et al, Int J Cancer, 15 Nov 2011;129(10):2315-27). For instance, properties of CSC in glioma in a mouse xenograft model were maintained by vascular endothelial cells (Calabrese C, et al, Cancer Cell, Jan 2007;11(1):69-82). Several molecules including interleukin 6 have been observed to play a role in tumor proliferation and hence, participate in maintaining tumorigenic and self-renewal potential of CSC. Moreover, the CSC niche might not only regulate CSCs traits but might also directly provide CSC features to non-CSC population.

What is the origin of CSC?

According to current thinking, CSC result from epithelial-mesenchymal transition (EMT) when cells switch from a polarized epithelial to a non-polarized mesenchymal cell type with stem cell properties, including migratory behavior, self-renewal and generation of differentiated progeny, and reduced responsiveness to conventional cancer therapies (Scheel C and Weinberg RA, Semin Cancer Biol, Oct 2012;22(5-6):396-403; Crews LA and Jamieson CH, Cancer Lett, 17 Aug 2012). Evidence is accumulating that cancers of distinct subtypes within an organ may derive from different ‘cells of origin’. The tumor cell of origin is the cell type from which the disease is derived after it undergoes oncogenic mutation. It might take a series of mutations to achieve the CSC phenotype (Visvader JE, Nature, 20 Jan 2011;469(7330):314-22). Also, CSCs have been reported to originate from stem cells in some cases.

Biomarkers for CSC

CSC targeting therapy could either eliminate CSCs by either killing them after differentiating them from other tumor population, and/or by disrupting their niche. Efficient eradication of CSCs may require the combined ablation of CSCs themselves and their niches. Identifying appropriate biomarkers of CSC is a very important aim for CSCs to be useful as targets of anti-cancer therapies in order to possibly prevent relapse. Using cell surface markers, CSCs have been isolated and purified from cancers of breast, brain, thyroid, cervix, lung, blood (leukemia), skin (melanoma), organs of the gastrointestinal and reproductive tracts, and the retina. The challenge, however, is that CSCs share similar markers with normal cells which makes CSCs targeting difficult as it would harm normal cells in the process. More recently, advanced techniques such as signal sequence trap (SST) PCR screening methods have been developed to identify a leukemia-specific stem cell marker (CD96). After a small subset of human AML cells displayed tumorigenic properties, Leukemia Stem Cells (LSCs) were identified as leukemia cells with CD23+/CD38+ markers. These cells closely resemble hematopeotic stem cells (HSCs) (Bonnet D and Dick JR, Nat Med, Jul 1997;3(7):730-7). In solid tumors, a significant discovery was made when CSCs in breast cancer were identified within the ESA+/CD44+/CD24low-neg population of mammary pleural effusion and tumor samples (Al-Hajj M, et al, PNAS USA, 1 Apr 200;100(7):3983-8).

After these two landmark publications, CSCs were identified in many more solid and hematopoietic human tumors as well. In addition, within a tumor type, CSC-enriched populations display heterogeneity in markers. For example, only 1% of breast cancer cells simultaneously express both reported CSC phenotypes ESA+/CD44+/

CD24low-neg and ALDH-1+ (Ginestier C, et al, Cell Stem Cell, 1 Nov 2007;1(5):555-67). The discrepancy might be due to different techniques used to identify the markers and also a reflection of the molecular heterogeneity within the tumors. Recent advances in genome wide expression profiling studies have led to the identification of different subtypes in a particular type of cancer. Breast cancer was recently classified into different subtypes and this genetic heterogeneity is likely paralleled by a heterogeneous CSC complexity.

Conclusion

A lot of research is currently underway on various aspects of CSCs including biomarker identification, cell of origin, and clinical trials targeting CSC population in cancer. The concept of CSCs has evolved quite a bit since their discovery. Recently, identification of high genetic heterogeneity within a tumor has been in focus and subsequently it has been observed that several CSC clones can coexist and compete with each other within a tumor. Adding complexity to their identity is the fact that CSCs may have unstable phenotypes and genotypes. Taken together, the dynamics associated with CSCs makes it difficult to identify reliable and robust biomarkers and develop efficient targeted therapies. Thus, a major thrust of research should be to focus on the unfolding of the dynamic identity of CSCs in tumor types and at different that might lead to the identification and targeting of highly specific CSCs biomarkers.

Reference

Jemal A, et al, CA Cancer J Clin, Mar-Apr 2011;61(2):69-90

Reya T, et al, Nature, 1 Nov 2001;414(6859):105-11

Trumpp A and Wiestler OD, et al, Nat Clin Pract Oncol, Jun 2008;5(6):337-47

Lapidot T, et al, Nature, 17 Feb 1994;367(6464):645-8

Singh SK, et al, Nature, 18 Nov 2004;432(7015):396-401

Dalerba P, et al, PNAS USA, 12 Jun 2007;104(24):10158-63

Hermann PC, et al, Cell Stem Cell, 13 Sep 2007;1(3):313-23

Wilson A, et al, Cell, 12 Dec 2008;135(6):1118-29

Li X,et al, J Natl Cancer Inst, 7 May 2008;100(9):672-9

Oravecz-Wilson KI, et al, Cancer Cell, 4 Aug 2009;16(2):137-48

Cabarcas SM, et al, Int J Cancer, 15 Nov 2011;129(10):2315-27

Calabrese C, et al, Cancer Cell, Jan 2007;11(1):69-82

Scheel C and Weinberg RA, Semin Cancer Biol, Oct 2012;22(5-6):396-403

Crews LA and Jamieson CH, Cancer Lett, 17 Aug 2012

Visvader JE, Nature, 20 Jan 2011;469(7330):314-22

Bonnet D and Dick JR, Nat Med, Jul 1997;3(7):730-7

Al-Hajj M, et al, PNAS USA, 1 Apr 200;100(7):3983-8

Ginestier C, et al, Cell Stem Cell, 1 Nov 2007;1(5):555-67

Baccelli I and Trumpp AJ, Cell Biol, 6 Aug 2012;198(3):281-93

Zhao L, et al, Eur Surg Res, 2012;49(1):8-15

Pharmaceutical Intelligence posts:

http://pharmaceuticalintelligence.com/2012/08/15/to-die-or-not-to-die-time-and-order-of-combination-drugs-for-triple-negative-breast-cancer-cells-a-systems-level-analysis/

Authors: Anamika Sarkar, PhD and Ritu Saxena, PhD

http://pharmaceuticalintelligence.com/2013/03/07/the-importance-of-cancer-prevention-programs-new-perceptions-for-fighting-cancer/ Author: Ziv Raviv, PhD

http://pharmaceuticalintelligence.com/2013/03/03/treatment-for-metastatic-her2-breast-cancer/ Reporter: Larry H Bernstein, MD

http://pharmaceuticalintelligence.com/2013/03/02/recurrence-risk-for-breast-cancer/

Larry H Bernstein, MD

http://pharmaceuticalintelligence.com/2013/02/14/prostate-cancer-androgen-driven-pathomechanism-in-early-onset-forms-of-the-disease/ Curator: Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2013/01/15/exploring-the-role-of-vitamin-c-in-cancer-therapy/ Curator: Ritu Saxena, PhD

http://pharmaceuticalintelligence.com/2013/01/10/the-molecular-pathology-of-breast-cancer-progression/ Author and reporter: Tilda Barliya PhD

http://pharmaceuticalintelligence.com/2012/11/30/histone-deacetylase-inhibitors-induce-epithelial-to-mesenchymal-transition-in-prostate-cancer-cells/ Reporter and Curator: Stephen J. Williams, PhD

http://pharmaceuticalintelligence.com/2012/10/22/blood-vessel-generating-stem-cells-discovered/ Reporter: Ritu Saxena, PhD

http://pharmaceuticalintelligence.com/2012/10/17/stomach-cancer-subtypes-methylation-based-identified-by-singapore-led-team/ Reporter: Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2012/09/17/natural-agents-for-prostate-cancer-bone-metastasis-treatment/ Reporter: Ritu Saxena, PhD

Read Full Post »

Imaging-biomarkers is Imaging-based tissue characterization

Posted in Advanced Drug Manufacturing Technology, Bio Instrumentation in Experimental Life Sciences Research, Biomarkers & Medical Diagnostics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Prevention: Research & Programs, Computational Biology/Systems and Bioinformatics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Drug Delivery Platform Technology, Ecosystems & Industrial Concentration in the Medical Device Sector, FDA Regulatory Affairs, FDA, CE Mark & Global Regulatory Affairs: process management and strategic planning - GCP, GLP, ISO 14155, Genomic Testing: Methodology for Diagnosis, Health Economics and Outcomes Research, HealthCare IT, Imaging-based Cancer Patient Management, International Global Work in Pharmaceutical, Medical Devices R&D and Inventions, Medical Devices R&D Investment, Medical Imaging Technology, Image Processing/Computing, MRI, CT, Nuclear Medicine, Ultra Sound, Personalized and Precision Medicine & Genomic Research, Pharmaceutical R&D Investment, Pharmacotherapy and Cell Activity, Population Health Management, Genetics & Pharmaceutical, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Technology Transfer: Biotech and Pharmaceutical, tagged bio-markers, biomarkers, Cancer - General, cancer management, imaging-bio-markers, MRI, MRI sequence, research, tissue characterisation on March 10, 2013| 3 Comments »

Imaging-biomarkers is Imaging-based tissue characterization

Author – Writer: Dror Nir, PhD

Article 11.1 Introduction by Dror Nir PhD

For everyone who is skeptical about the future role of imaging-based tissue chracterisation in the management of cancer, the following “Statement paper” ESR statement on the stepwise development of imaging biomarkers published online: 9 February 2013, by the European Society of Radiology (ESR), should provide substantial reassurance that this kind of technology will become a must! In support of this claim I quote the following information:

“The European Society of Radiology and its related European Institute for Biomedical Imaging Research (EIBIR) should have a relevant role in coordinating future developments of biomarkers and in the assessment and validation of imaging biomarkers as surrogate end points.

Acknowledgements

This paper was kindly prepared by the ESR Subcommittee on Imaging Biomarkers (Chairperson: Bernard Van Beers. Research Committee Chairperson: Luis Martí-Bonmatí. Members: Marco Essig, Thomas Helbich, Celso Matos, Wiro Niessen, Anwar Padhani, Harriet C. Thoeny, Siegfried Trattnig, Jean-Paul Vallée. Co-opted members: Peter Brader, Nicolas Grenier) on behalf of the European Society of Radiology (ESR) and with the help of Sabrina Doblas, INSERM U773, Paris, France.

It was approved by the ESR Executive Council in December 2012..”

According to ESR: “There is increasing interest in developing the quantitative imaging of biomarkers in personalised medicine”. In this perspective, “Biomarkers” are tissue properties that can be quantitatively and reproducibly measured by imaging devices. One example for a major unmet need, which I found to be most interesting is the imaging-based detection of tumor invasiveness.

Quoting from the paper: ” Biomarkers are defined as “characteristics that are objectively measured and evaluated as indicators of normal biological processes, pathological processes, or pharmaceutical responses to a therapeutic intervention” [1]. Broadly, biomarkers fall into two categories: bio-specimen biomarkers, including molecular biomarkers and genetic biomarkers, and bio-signal biomarkers or imaging biomarkers. Bio-specimen biomarkers are obtained by removing a sample from a patient. Examples of these molecular biomarkers are genes and proteins detected from fluids or tissue samples. Bio-signal biomarkers remove no material from the patient, but rather detect and analyse an electromagnetic, photonic or acoustic signal emitted by the patient [2]. These imaging biomarkers have the advantage of being non-invasive, spatially resolved and repeatable [3]. They are of particular interest if they can overcome the limitations of the established histological “gold standards”. Indeed, invasive reference examinations, such as biopsy, can be inconclusive, are non-representative of the whole tissue (which is a tremendous limitation when assessing malignant tumours, which are known to be heterogeneous) and possess non-negligible levels of mortality and morbidity.

Genetic biomarkers indicate whether a disease may occur, but they are usually inefficient to assess the presence and stage of a disease. Similar to molecular biomarkers, imaging biomarkers can be used for early detection of diseases, staging and grading, and predicting or assessing the response to treatment [3]. Accordingly, because of their relative lower cost compared with imaging, molecular biomarkers may be more appropriate for disease screening and early detection than imaging biomarkers. With their high sensitivity, molecular biomarkers could also detect subclinical stages of disease before any morphological or functional change is detectable on imaging. In contrast, imaging biomarkers are often more useful than molecular biomarkers for disease staging, and also grading and for assessing tumour response, because localised information is crucial.”

The main messages ESR wishes to deliver in this paper are that:

• Using imaging-biomarkers to streamline drug discovery and disease progression will drive a huge advancement in healthcare.

• The clinical qualification and validation of imaging biomarkers technology pose challenges, mainly in establishing the accuracy and reproducibility of such techniques. In that respect, agreements on standards and evaluation methods (e.g. clinical studies design) is imperative.

• There should be high motivation to pursue the development of imaging-biomarkers as the “clinical value of new biomarkers is of the highest priority in terms of patient management, assessing risk factors and disease prognosis.”

The paper deals to a great extent with the requirements on accuracy, reproducibility, standardization and quality control from the process of developing imaging-biomarkers:

“Accuracy: Before being routinely used in the clinic, imaging biomarkers must be validated. Determining the accuracy implies calculating the sensitivity and specificity of the biomarker when compared with a biological process, such as tumour necrosis, which can be assessed at histopathological examination… [6–9] [10, 11]

Reproducibility: Repeatability (measurements at short intervals on the same subjects using the same equipment in the same centres) and reproducibility (measurements at short intervals on the same subjects using different facilities in the same and different centres) studies must be conducted for image acquisition and image analysis…. Reproducibility studies are now very often included in scientific papers, as advised by the “standards for reporting of diagnostic accuracy” (STARD) criteria and should ideally include Bland-Altman plots and results of coefficients of repeatability [16, 17].

Standardisation: Standardisation relates to the establishment of norms or requirements about technical aspects. In the development of imaging biomarkers, two main aspects should be considered: Standardisation of image acquisition and Standardisation of image analysis… [18] [19–21] [22] [27, 28] [31–33]

Quality control: Adequate phantoms could be used to validate, on a day-to-day basis, that the biomarker stays robust and to avoid any drift in the machine, acquisition or processing protocol…. [34] [30, 35] [36] [37] [23].”

The proposed development workflow:

“Similar to new drugs, the development of biomarkers has to pass along a pipeline going from discovery, through verification in different laboratories, validation and qualification before they can be used in clinical routine. Validation includes the determination of the accuracy and the precision (reproducibility) of the biomarker and standardisation concerns both acquisition and analysis. Qualification, defined as a “graded, fit-for-purpose evidentiary process linking a biomarker with biological processes and clinical end-points”, is a validation process in large cohorts of patients involving multiple centres, similar to phase III clinical trials, to obtain regulatory approval as surrogate endpoints [4]. A more extensive path to biomarker development has been reported [5]. The first step is the proof of concept, which defines any specific change relevant to the disease that can be studied using the available imaging and computational techniques. The relationship between this change and the presence, grading and response to treatment of the disease constitutes the proof of mechanism. The images needed to extract the biomarker must be appropriate (in terms of resolution, signal and contrast behaviour). Preparation of images relates to improving the data before the analysis (such as segmentation, filtering, interpolation or registration). The analysis and modelling of the signal by computational numerical adjustment of a mathematical model allow extracting the needed information (such as structural, physical, chemical, biological and functional properties). After this voxel-by-voxel computation, the spatial distribution of the biomarker can be depicted by parametric images, defined as derived secondary images which pixels represent the distribution values of a given parameter. Multivariate parametric images obtained by statistical modelling of the relevant parameters allow the reduction of data and a clear definition of the defined disease target. The abnormal values should be defined and measured through histogram analysis. A pilot test on a small sample of subjects, with and without the disease, has to be performed to validate the process—also called proof of principle—and to evaluate the influence of potential variations related to age, sex or any other source of biases. Finally, proofs of efficacy and effectiveness on larger and well-defined series of patients will show the ability of a biomarker to measure the clinical endpoint (Fig. 1).”

Steps for the development of imaging biomarkers (adapted from [5])

The authors admit that the requirement posed on development of imaging-biomarkers represents a huge challenge and they try to offer ideas, mainly taken from the “MRI experience” to overcome certain hurdles. There is one important point on which they do not discuss: the definition of appropriate reference test. It is my own experience, based on many study protocols I developed in the past decade, that without reaching an agreement on that point, the development of imaging-biomarkers will just move in circles. Note, that today’s most “acceptable” reference test is histopathology, which everyone admits (as well mentioned in this paper); suffers many limitations. When it comes to validating imaging-biomarkers, the need to accurately match imaging products with histopathology is an additional major hurdle.

This is why, I see as a necessary step, to develop “real-time” imaging based tissue characterization combined with in-situ imaging-based histology.

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Alzheimer’s Disease Conundrum – Are We Near the End of the Puzzle?

Posted in Advanced Drug Manufacturing Technology, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Cell Biology, Signaling & Cell Circuits, Cerebrovascular and Neurodegenerative Diseases, Chemical Genetics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Genome Biology, Genomic Testing: Methodology for Diagnosis, Human Immune System in Health and in Disease, Innovations in Neurophysiology & Neuropsychology, International Global Work in Pharmaceutical, Medical Imaging Technology, Image Processing/Computing, MRI, CT, Nuclear Medicine, Ultra Sound, Molecular Genetics & Pharmaceutical, Pharmaceutical Industry Competitive Intelligence, Proteomics, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Technology Transfer: Biotech and Pharmaceutical, tagged Alois Alzheimer, Alzheimer, Alzheimer Disease, Alzheimers Disease, ApoE, BCHE brain imaging, Beta amyloid, biomarkers, cellular inflammatiory response, CLU, conitive loss, Decode Genetics, dementia, Icelanders, innovative applications, Kári Stefánsson, mapping, Massachusetts General Hospital, Picalm, plaque regression, Tau protein on March 9, 2013| 1 Comment »

Alzheimer’s Genomic Diagnosis and Treatment

Curator: Larry H Bernstein, MD, FCAP

Gene Mutation Protects Against Alzheimer’s

by Greg Miller on 11 July 2012
Brain preserver. A newly discovered gene mutation appears to protect against Alzheimer’s disease. Credit: Alzheimer’s Disease Education and Referral Center/NIA/NIH
http://news.sciencemag.org/sciencenow/2012/07/gene-mutation-protects-against-a.html

A rare mutation that alters a single letter of the genetic code protects people from the

memory-robbing dementia of Alzheimer’s disease.

The DNA change may inhibit the buildup of β amyloid, the

protein fragment that forms the hallmark plaques in the brains of Alzheimer’s patients.
The mutation affects a gene called APP,
which encodes a protein that gets broken down into pieces,
including β amyloid.

Researchers previously identified more than 30 mutations to APP, none of them good. Several of these changes increase β amyloid formation and cause

• a devastating inherited form of Alzheimer’s that afflicts people in their 30s and 40s—

• much earlier than the far more common “late-onset” form of Alzheimer’s

that typically strikes people their 70s and 80s.

The new mutation, discovered from whole-genome data from 1795 Icelanders for variations in APP that protect against Alzheimer’s, appears to do the opposite. The mutation interferes with one of the enzymes that breaks down the APP protein and causes a 40% reduction in β amyloid formation

New pharmacological strategies for treatment of Alzheimer’s disease: focus on disease modifying drugs.
Salomone S, Caraci F, Leggio GM, Fedotova J, Drago F.
University of Catania, Viale Andrea Doria 6, Catania, Italy.
Br J Clin Pharmacol. 2012 Apr;73(4):504-17. doi: 10.1111/j.1365-2125.2011.04134.x.

Current approved drug treatments for Alzheimer disease (AD) include

cholinesterase inhibitors (donepezil, rivastigmine, galantamine) and the
NMDA receptor antagonist memantine.

These drugs provide symptomatic relief but poorly affect the progression of the disease. Drug discovery has been directed, in the last 10 years, to develop ‘disease modifying drugs’ hopefully able to counteract the progression of AD. Because in a chronic, slow progressing pathological process, such as AD, an early start of treatment enhances the chance of success,

it is crucial to have biomarkers for early detection of AD-related brain dysfunction,
- usable before clinical onset.

Reliable early biomarkers need therefore to be prospectively tested for predictive accuracy,

with specific cut off values validated in clinical practice.

Disease modifying drugs developed so far include drugs to

reduce β amyloid (Aβ) production,
drugs to prevent Aβ aggregation,
drugs to promote Aβ clearance,
drugs targeting tau phosphorylation and assembly

None of these drugs has demonstrated efficacy in phase 3 studies. The failure of clinical trials with disease modifying drugs raises a number of questions, spanning from

methodological flaws to
fundamental understanding of AD pathophysiology and biology.

Diagnostic criteria applicable to presymptomatic stages of AD have now been published.

These new criteria may impact on drug development, such that future trials on disease modifying drugs will include populations susceptible to AD, before clinical onset. http://www.ncbi.nlm.nih.gov/pubmed/22035455

Gene mutation defends against Alzheimer’s disease
Rare genetic variant suggests a cause and treatment for cognitive decline.
Ewen Callaway 11 July 2012
http://www.nature.com/news/gene-mutation-defends-against-alzheimer-s-disease-1.10984

J. NIETH/CORBIS
Almost 30 million people live with Alzheimer’s disease worldwide, a staggering health-care burden that is expected to quadruple by 2050. Yet doctors can offer no effective treatment, and scientists have been unable to pin down the underlying mechanism of the disease.
Research published this week offers some hope on both counts – few people carry a genetic mutation that naturally prevents them from developing the condition – 0.5% of Icelanders have a protective gene, as are 0.2–0.5% of Finns, Swedes and Norwegians. Icelanders who carry it have a 50% better chance of reaching age 85, are more than five times more likely to reach it 85 without Alzheimer’s. The mutation seems to put a brake on the milder mental deterioration that most elderly people experience. Carriers are about 7.5 times more likely than non-carriers to reach the age of 85 without major cognitive decline, and perform better on the cognitive tests that are administered thrice yearly to Icelanders who live in nursing homes.
The discovery not only confirms the principal suspect that is responsible for Alzheimer’s, it also suggests that the disease could be

an extreme form of the cognitive decline seen in many older people.

The mutation — the first ever found to protect against the disease — lies in a gene that produces

amyloid-β precursor protein (APP),
which has an unknown role in the brain

APP was discovered 25 years ago in patients with rare,

inherited forms of Alzheimer’s that strike in middle age.
In the brain, APP is broken down into a smaller molecule called amyloid-β.

Visible clumps, or plaques, of amyloid-β found in the autopsied brains of patients are a hallmark of Alzheimer’s.
Scientists have long debated whether the plaques are a cause of the neurodegenerative condition

or a consequence of other biochemical changes associated with the disease.

The latest finding supports other genetics studies blaming amyloid-β, according to Rudolph Tanzi, a neurologist at the Massachusetts General Hospital in Boston and a member of one of the four teams that discovered APP’s role in the 1980s.
If amyloid-β plaques were confirmed as the cause of Alzheimer’s, it would bolster efforts to develop drugs that block their formation, says Kári Stefánsson, chief executive of deCODE Genetics in Reykjavik, Iceland, who led the latest research. He and his team first discovered the mutation by comparing the complete genome sequences of 1,795 Icelanders with their medical histories. The researchers then studied the variant in nearly 400,000 more Scandinavians.
This suggests that Alzheimer’s disease and cognitive decline are two sides of the same coin, with a common cause — the build-up of amyloid-β plaques in the brain, something seen to a lesser degree in elderly people who do not develop full-blown Alzheimer’s. A drug that mimics the effects of the mutation, might slow cognitive decline as well as prevent Alzheimer’s.
Stefánsson and his team discovered that the mutation introduces a single amino-acid alteration to APP. This amino acid is close to the site where an enzyme called

β-secretase 1 (BACE1) ordinarily snips APP into smaller amyloid-β chunks —
and the alteration is enough to reduce the enzyme’s efficiency.

Stefánsson’s study suggests that blocking β-secretase from cleaving APP has the potential to prevent Alzheimer’s, but Philippe Amouyel, an epidemiologist at the Pasteur Institute in Lille, France, says “it is very difficult to identify the

precise time when this amyloid toxic effect could still be modified”.

“If this effect needs to be blocked as early as possible in life to protect against Alzheimer’s disease, we will need to propose a new design for clinical trials” to identify an effective treatment.

The results demonstrate that whole-genome sequencing can uncover very rare mutations that might offer insight into common diseases.

disease risk, may be determined by genetic variants that slightly tilt the odds of developing disease
In this case a rare mutant may provide very key mechanistic insights into Alzheimer’s

Jonsson, T. et al. Nature http://dx.doi.org/10.1038/nature11283 (2012).
Kang, J. et al. Nature 325, 733–736 (1987).
Goldgaber, D., Lerman, M. I., McBride, O. W., Saffiotti, U. & Gajdusek, D. C. Science 235, 877–880 (1987).

BHCE genetic data combined with brain imaging using agent florbetapir connects the BHCE gene to AD plaque buildup. BHCE is an enzyme that breaks down acetylcholine in the brain, which is depleted early in the disease and results in memory loss. http://www.genengnews.com/

New Alzheimer’s Genes Found
Gigantic Scientific Effort Discovers Clues to Treatment, Diagnosis of Alzheimer’s Disease
By Daniel J. DeNoon
WebMD Health News Reviewed by Laura J. Martin, MD
http://www.webmd.com/alzheimers/news/20110403/new-alzheimers-genes-found

A massive scientific effort has found five new gene variants linked to Alzheimer’s disease. The undertaking involved analyzing the genomes of nearly 40,000 people with and without Alzheimer’s. This study was undertaken by two separate research consortiums in the U.S. and in Europe, which collaborated to confirm each other’s results.
Four genes had previously been linked to Alzheimer’s. Three of them affect only the risk of relatively rare forms of Alzheimer’s. The fourth is APOE, until now the only gene known to affect risk of the common, late-onset form of Alzheimer’s. Roughly 27% of Alzheimer’s disease can be attributed to the five new gene variants. Even though Alzheimer’s is a very complex disease, the new findings represent a large chunk of Alzheimer’s risk, according to Margaret A. Pericak-Vance, PhD, of the U.S. consortium –

20% of the causal risk of Alzheimer’s disease and
32% of the genetic risk.

Alzheimer’s Tied to Mutation Harming Immune Response
By GINA KOLATA Published: November 14, 2012 in NY Times
http://www.nytimes.com/2012/11/15/health/gene-mutation-that-hobbles-immune-response-is-linked-to-alzheimers.html?_r=0
Alzheimer’s researchers and drug companies have for years concentrated on one hallmark of Alzheimer’s disease: the production of toxic shards of a protein that accumulate in plaques on the brain.
Two groups of researchers working from entirely different starting points have converged on a mutated gene involved in another aspect of Alzheimer’s disease:

the immune system’s role in protecting against the disease.

The mutation is suspected of interfering with

the brain’s ability to prevent the buildup of plaque.

When the gene is not mutated, white blood cells in the brain spring into action,

gobbling up and eliminating the plaque-forming toxic protein, beta amyloid.

As a result, Alzheimer’s can be staved off or averted. People with the mutated gene have a threefold to fivefold increase in the likelihood of developing Alzheimer’s disease in old age.

Comparing Differences

Dr. Julie Williams’s, Cardiff, Wales (European team leader) report identified CLU and Picalm. A second study published in Nature Genetics, by Philippe Amouyel from Institut Pasteur de Lille in France, pinpointed CLU and CR1. The greatest inherited risk comes from the APOE gene, discovered in 1993 by a team led by Allen Roses, now director of the Deane Drug Discovery Institute at Duke UMC, in Durham, North Carolina.
The findings “are beginning to give us insight into the biology, but I don’t think you can expect treatments overnight,” Dr. Michael Owen (Cardiff, Wales) said. Instead, the genes will show a mosaic of risk, and “the key issue is what hand of cards you’re dealt,” he said.

Promise for Early Diagnosis
BHCE genetic data combined with brain imaging using agent florbetapir connects the BHCE gene to AD plaque buildup. BHCE is an enzyme that breaks down acetylcholine in the brain, which is depleted early in the disease and results in memory loss.

Dr. Bernstein’s comments:

There has been a long history of failure of drugs to slow down the progression of Alzheimer’s. Regression of the plaques has not corresponded with retention of cognitive ability, which has been behind the arguments over beta amyloid or tau.
We now have two particularly interesting mutations –
1. ApoE gene mutation that increases risk
2. APP mutation that quite dramatically affects retention of cognition

Alzheimer’s Disease: Green Tea Extract Found To Interfere With The Formation Of Amyloid Plaques (medicalnewstoday.com)
Aging Brain – The Specific Gene That Causes It (belmarrahealth.com)
The Pathogenic Aβ43 Is Enriched in Familial and Sporadic Alzheimer Disease (plosone.org)
Damaged blood vessels loaded with amyloid worsen cognitive impairment in Alzheimer’s disease (eurekalert.org)
Major step toward an Alzheimer’s vaccine (nextbigfuture.com)
Damaged blood vessels loaded with amyloid worsen cognitive impairment in Alzheimer’s disease (medicalxpress.com)
Top Signs You are at Risk for Alzheimer’s (belmarrahealth.com)
A Reliable Way to Detect Endogenous Murine β-Amyloid (plosone.org)

β-amyloid fibrils. (Photo credit: Wikipedia)

English: PET scan of a human brain with Alzheimer’s disease (Photo credit: Wikipedia)

Depiction of amyloid precursor protein processing, created by I. Peltan Ipeltan (Photo credit: Wikipedia)

English: Diagram of how microtubules desintegrate with Alzheimer’s disease Français : La protéine Tau dans un neurone sain et dans un neurone malade Español: Esquema que muestra cómo se desintegran los microtúbulos en la enfermedad de Alzheimer (Photo credit: Wikipedia)

English: Histopathogic image of senile plaques seen in the cerebral cortex in a patient with presenile onset of Alzheimer disease. Bowdian stain. The same case as shown in a file “Alzheimer_dementia_(1)_presenile_onset.jpg”. (Photo credit: Wikipedia)

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Diagnosing Lung Cancer in Exhaled Breath using Gold Nanoparticles

Posted in Bio Instrumentation in Experimental Life Sciences Research, Biomarkers & Medical Diagnostics, BioSimilars, CANCER BIOLOGY & Innovations in Cancer Therapy, Chemical Biology and its relations to Metabolic Disease, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Medical Devices R&D Investment, Nanotechnology for Drug Delivery, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, Technology Transfer: Biotech and Pharmaceutical, tagged biomarkers, diagnostics, exhaled breath, Gas chromatography, gold nanoparticles, Lung cancer, nanotechnology, sensors, tests, volatile organic compounds (VOCs) on December 1, 2012| 8 Comments »

Diagnosing Lung Cancer in Exhaled Breath using Gold Nanoparticles

Reporter-curator: Tilda Barliya PhD

Authors: Gang Peng, Ulrike Tisch, Orna Adams¹, Meggie Hakim, Nisrean Shehada, Yoav Y. Broza, Salem Billan, Roxolyana Abdah-Bortnyak, Abraham Kuten & Hossam Haick. (NATURE NANOTECHNOLOGY | VOL 4 | OCTOBER 2009 |)

Abstract:

Conventional diagnostic methods for lung cancer1,2 are unsuitable for widespread screening, because they are expensive and occasionally miss tumours. Gas chromatography/mass spectrometry studies have shown that several volatile organic compounds, which normally appear at levels of 1–20 ppb in healthy human breath, are elevated to levels between 10 and 100 ppb in lung cancer patients. Here we show that an array of sensors based on gold nanoparticles can rapidly distinguish the breath of lung cancer patients from the breath of healthy individuals in an atmosphere of high humidity. In combination with solidphase microextraction, gas chromatography/mass spectrometry was used to identify 42 volatile organic compounds that represent lung cancer biomarkers. Four of these were used to train and optimize the sensors, demonstrating good agreement between patient and simulated breath samples. Our results show that sensors based on gold nanoparticles could form the basis of an inexpensive and non-invasive diagnostic tool for lung cancer. (http://www.nature.com/nnano/journal/v4/n10/abs/nnano.2009.235.html) (lnbd.technion.ac.il/NanoChemistry/SendFile.asp?DBID=1…1…)

Introduction:

Lung cancer accounts for 28% of cancer-related deaths. Approximately 1.3 million people die worldwide every year. Breath testing is a fast, non-invasive diagnostic method that links specific volatile organic compounds (VOCs) in exhaled breath to medical conditions. Gas chromatography/mass spectrometry (GC-MS), ion flow tube mass spectrometry10, laser absorption spectrometry,infrared spectroscopy, polymer-coated surface acoustic wave sensors and coated quartz crystal microbalance sensors have been used for this purpose. However, these techniques are expensive, slow, require complex instruments and, furthermore, require pre-concentration of the biomarkers (that is, treating the biomarkers by a process to increase the relative concentration of the biomarkers to a level that can be detected by the specific technique) to improve detection.

Here, we report a simple, inexpensive, portable sensing technology to distinguish the breath of lung cancer patients from healthy subjects without the need to pre-treat the exhaled breath in any way (see also refs 14–16 for the diagnosis of lung cancer by sensing technology that is based on arrays of polymer/carbon black sensors). Our study consisted of four phases and included volunteers aged 28–60 years. Samples were collected from 56 healthy controls and 40 lung cancer patients after clinical diagnosis using conventional methods and before chemotherapy or other treatment.

In the first phase, we collected exhaled alveolar breath of lung cancer patients and healthy subjects using an ‘offline’ method. This method was designed to avoid potential errors arising from the failure to distinguish endogenous compounds from exogenous ones in the breath and to exclude nasal entrainment of the gas. Exogenous VOCs can be either directly absorbed through the lung via the inhaled breath or indirectly through the blood or skin. Endogenous VOCs are generated by cellular biochemical processes in the body and may provide insight into the body’s function

In the second phase, we identified the VOCs that can serve as biomarkers for lung cancer in the breath samples and determined their relative compositions, using GC-MS in combination with solidphase microextraction (SPME). GC-MS analysis identified over 300–400 different VOCs per breath sample, with .87% reproducibility for a specific volunteer examined multiple times over a period of six months. Forward stepwise discriminant analysis identified 33 common VOCs that appear in at least 83% of the patients but in fewer than 83% of the healthy subjects

The compounds that were observed in both healthy breath and lung cancer breath were presented not only at different concentrations but also in distinctively different mixture compositions.

Further forward stepwise discriminant analysis revealed nine uncommon VOCs that appear in at least 83% of the patients but not in the majority (83%) of healthy subjects. This additional class of VOCs has not been recognized in earlier GC-MS studies.

In spite of these advances in the GC-MS analysis, these data certainly do not account for all the VOCs present in the exhaled breath samples, because the pre-concentration technique can be thought of as a solid phase that extracts only part of the analytes present in the examined phase and, subsequently, releases only part of the extracted analytes.

So, it is likely that the actual mixture of VOCs to which, for example, an array of gold nanoparticle sensors would be responding is different from that obtained by GC-MS.

In the third phase of this study we designed an array of nine crossreactive chemiresistors, in which each sensor was widely responsive to a variety of odorants for the detection of lung cancer by means of breath testing. We used chemiresistors based on assemblies of 5-nm gold nanoparticles with different organic functionalities (dodecanethiol, decanethiol, 1-butanethiol, 2-ethylhexanethiol, hexanethiol, tert-dodecanethiol, 4-methoxy-toluenethiol, 2-mercaptobenzoxazole and 11-mercapto-1-undecanol).

Chemiresistors based on functionalized gold nanoparticles combine the advantages of organic specificity with the robustness and processability of inorganic materials.

The response of the nine-sensor array to both healthy and lung cancer breath samples was analysed using principal component analysis . It can be seen that there is no overlap of the lung cancer and healthy patterns.

The PCA of the healthy control group revealed that the set of gold nanoparticles sensors was not influenced by characteristics such as gender, age or smoking habits, thus strengthening the ability of the sensors to discriminate between healthy and cancerous breath. Experiments with a wider population of volunteers to thoroughly probe the influence of diet, alcohol consumption,metabolic state and genetics are under way and will be published elsewhere.

Summary:

To summarize, we have demonstrated that an array of chemiresistors based on functionalized gold nanoparticles in combination with pattern recognition methods can distinguish between the breath of lung cancer patients and healthy controls, without the need for dehumidification or pre-concentration of the lung cancer biomarkers. Our results show great promise for fast, easy and cost-effective diagnosis and screening of lung cancer. The developed devices are expected to be relatively inexpensive, portable and amenable to use in widespread screening, making them potentially valuable in saving millions of lives every year. Given the impact of the rising incidence of cancer on health budgets worldwide, the proposed technology will be a significant saving for both private and public health expenditures. The potential exists for using the proposed technology to diagnose other conditions and diseases, which could mean additional cost reductions and enhanced opportunities to save lives.

Ref:

1. Gang Peng, Ulrike Tisch, Orna Adams, Meggie Hakim, Nisrean Shehada, Yoav Y. Broza, Salem Billan, Roxolyana Abdah-Bortnyak, Abraham Kuten& Hossam Haick. Diagnosing lung cancer in exhaled breath using gold nanoparticles. Nature Nanotechnology 4, 669 – 673 (2009) http://www.nature.com/nnano/journal/v4/n10/abs/nnano.2009.235.html

2. http://lungcancer.about.com/od/diagnosisoflungcancer/a/diagnosislungca.htm

3. http://metabolomx.com/2011/12/15/metabolomx-test-detects-lung-cancer-from-breath/

4. http://www.chestnet.org/accp/pccsu/medical-applications-exhaled-breath-analysis-and-testing?page=0,3

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Demonstration of a diagnostic clinical laboratory neural network agent applied to three laboratory data conditioning problems

Posted in Bio Instrumentation in Experimental Life Sciences Research, Biomarkers & Medical Diagnostics, Computational Biology/Systems and Bioinformatics, HealthCare IT, Medical Devices R&D Investment, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Statistical Methods for Research Evaluation, tagged ANN, Artificial intelligence, artificial neural network, biomarkers, breast cancer, Brooklyn, cardiac arrhythmias, congestive heart failure, diagnostics, Google, Google Science Fair, informatics, myocardial infarct, Neural network, predictive analytics, preparatory clustering, training ANN on clusters on August 13, 2012| 7 Comments »

Demonstration of a diagnostic clinical laboratory neural network agent applied to three laboratory data conditioning problems

Izaak Mayzlin Larry Bernstein, MD

Principal Scientist, MayNet Technical Director

Boston, MA Methodist Hospital Laboratory, Brooklyn, NY

Our clinical chemistry section services a hospital emergency room seeing 15,000 patients with chest pain annually. We have used a neural network agent, MayNet, for data conditioning. Three applications are – troponin, CKMB, EKG for chest pain; B-type natriuretic peptide (BNP), EKG for congestive heart failure (CHF); and red cell count (RBC), mean corpuscular volume (MCV), hemoglobin A2 (Hgb A2) for beta thalassemia. Three data sets have been extensively validated prior to neural network analysis using receiver-operator curve (ROC analysis), Latent Class Analysis, and a multinomial regression approach. Optimum decision points for classifying using these data were determined using ROC (SYSTAT, 11.0), LCM (Latent Gold), and ordinal regression (GOLDminer). The ACS and CHF studies both had over 700 patients, and had a different validation sample than the initial exploratory population. The MayNet incorporates prior clustering, and sample extraction features in its application. Maynet results are in agreement with the other methods.

Introduction: A clinical laboratory servicing a hospital with an emergency room seeing 15,000 patients with chest pain to produce over 2 million quality controlled chemistry accessions annually. We have used a neural network agent, MayNet, to tackle the quality control of the information product. The agent combines a statistical tool that first performs clustering of input variables by Euclidean distances in multi-dimensional space. The clusters are trained on output variables by the artificial neural network performing non-linear discrimination on clusters’ averages. In applying this new agent system to diagnosis of acute myocardial infarction (AMI) we demonstrated that at an optimum clustering distance the number of classes is minimized with efficient training on the neural network. The software agent also performs a random partitioning of the patients’ data into training and testing sets, one time neural network training, and an accuracy estimate on the testing data set. Three examples to illustrate this are – troponin, CKMB, EKG for acute coronary syndrome (ACS); B-type natriuretic peptide (BNP), EKG for the estimation of ejection fraction in congestive heart failure (CHF); and red cell count (RBC), mean corpuscular volume (MCV), hemoglobin A2 (Hgb A2) for identifying beta thalassemia. We use three data sets that have been extensively validated prior to neural network analysis using receiver-operator curve (ROC analysis), Latent Class Analysis, and a multinomial regression approach.

In previous studies^1,2 CK-MB and LD1 sampled at 12 and 18 hours postadmission were near-optimum times used to form a classification by the analysis of information in the data set. The population consisted of 101 patients with and 41 patients without AMI based on review of the medical records, clinical presentation, electrocardiography, serial enzyme and isoenzyme assays, and other tests. The clinical or EKG data, and other enzymes or sampling times were not used to form a classification but could be handled by the program developed. All diagnoses were established by cardiologist review. An important methodological problem is the assignment of a correct diagnosis by a “gold standard” that is independent of the method being tested so that the method tested can be suitably validated. This solution is not satisfactory in the case of myocardial infarction because of the dependence of diagnosis on a constellation of observations with different sensitivities and specificities. We have argued that the accuracy of diagnosis is associated with the classes formed by combined features and has greatest uncertainty associated with a single measure.

Methods: Neural network analysis is by MayNet, developed by one of the authors. Optimum decision points for classifying using these data were determined using ROC (SYSTAT, 11.0), LCM (Latent Gold)³, and ordinal regression (GOLDminer)⁴. Validation of the ACS and CHF study sets both had over 700 patients, and all studies had a different validation sample than the initial exploratory population. The MayNet incorporates prior clustering, and sample extraction features in its application. We now report on a new classification method and its application to diagnosis of acute myocardial infarction (AMI). This method is based on the combination of clustering by Euclidean distances in multi-dimensional space and non-linear discrimination fulfilled by the Artificial Neural Network (ANN) trained on clusters’ averages. These studies indicate that at an optimum clustering distance the number of classes is minimized with efficient training on the ANN. This novel approach to ANN reduces the number of patterns used for ANN learning and works also as an effective tool for smoothing data, removing singularities, and increasing the accuracy of classification by the ANN. The studies conducted involve training and testing on separate clinical data sets, which subsequently achieves a high accuracy of diagnosis (97%).

Unlike classification, which assumes the prior definition of borders between classes^5,6, clustering procedure includes establishing these borders as a result of processing statistical information and using a given criteria for difference (distance) between classes. We perform clustering using the geometrical (Euclidean) distance between two points in n-dimensional space, formed by n variables, including both input and output variables. Since this distance assumes compatibility of different variables, the values of all input variables are linearly transformed (scaled) to the range from 0 to 1.

The ANN technique for readers accustomed to classical statistics can be viewed as an extension of multivariate regression analyses with such new features as non-linearity and ability to process categorical data. Categorical (not continuous) variables represent two or more levels, groups, or classes of correspondent feature, and in our case this concept is used to signify patient condition, for example existence or not of AMI.

The ANN is an acyclic directed graph with input and output nodes corresponding respectively to input and output variables. There are also “intermediate” nodes, comprising so called “hidden” layers. Each node n_jis assigned the value x_jthat has been evaluated by the node’s “processing” element, as a non-linear function of the weighted sum of values x_i of nodes n_i, connected with n_j by directed edges (n_i, n_j).

x_j = f(w_i(1),jx_i(1) + w_i(2),jx_i(2) + … + w_i(l),jx_i(l)),

where x_kis the value in node n_kand w_k,j is the “weight” of the edge (n_k, n_j). In our research we used the standard function f(x), “sigmoid”, defined as f(x)=1/(1+exp(-x)). This function is suitable for categorical output and allows for using an efficient back-propagation algorithm⁷ for calculating the optimal values of weights, providing the best fit for learning set of data, and eventually the most accurate classification.

Process description: We implemented the proposed algorithm for diagnosis of AMI. All the calculations were performed on PC with Pentium 3 Processor applying the authors’ unique Software Agent Maynet. First, using the automatic random extraction procedure, the initial data set (139 patients) was partitioned into two sets — training and testing. This randomization also determined the size of these sets (96 and 43, respectively) since the program was instructed to assign approximately 70 % of data to the training set.

The main process consists of three successive steps: (1) clustering performed on training data set, (2) neural network’s training on clusters from previous step, and (3) classifier’s accuracy evaluation on testing data.

The classifier in this research will be the ANN, created on step 2, with output in the range [0,1], that provides binary result (1 – AMI, 0 – not AMI), using decision point 0.5.

In this demonstartion we used the data of two previous studies^1,2 with three patients, potential outliers, removed (n = 139). The data contains three input variables, CK-MB, LD-1, LD-1/total LD, and one output variable, diagnoses, coded as 1 (for AMI) or 0 (non-AMI).

Results: The application of this software intelligent agent is first demonstrated here using the initial model. Figures 1-2 illustrate the history of training process. One function is the maximum (among training patterns) and lower function shows the average error. The latter defines duration of training process. Training terminates when the average error achieves 5%.

There was slow convergence of back-propagation algorithm applied to the training set of 96 patients. We needed 6800 iterations to achieve the sufficiently small (5%) average error.

Figure 1 shows the process of training on stage 2. It illustrates rapid convergence because we deal only with 9 patterns representing the 9 classes, formed on step 1.

Table 1 illustrates the effect of selection of maximum distance on the number of classes formed and on the production of errors. The number of classes increased with decreasing distance, but accuracy of classification does not decreased.

The rate of learning is inversely related to the number of classes. The use of the back-propagation to train on the entire data set without prior processing is slower than for the training on patterns.

Figures 2 is a two-dimensional projection of three-dimensional space of input variables CKMB and LD1 with small dots corresponding to the patterns and rectangular as cluster centroids (black – AMI, white – not AMI).

We carried out a larger study using troponin I (instead of LD1) and CKMB for the diagnosis of myocardial infarction (MI). The probabilities and odds-ratios for the TnI scaled into intervals near the entropy decision point are shown in Table 2 (N = 782). The cross-table shows the frequencies for scaled TnI results versus the observed MI, the percent of values within MI, and the predicted probabilities and odds-ratios for MI within TnI intervals. The optimum decision point is at or near 0.61 mg/L (the probability of MI at 0.46-0.6 mg/L is 3% and the odds ratio is at 13, while the probability of MI at 0.61-0.75 mg/L is 26% at an odds ratio of 174) by regressing the scaled values.

The RBC, MCV criteria used were applied to a series of 40 patients different than that used in deriving the cutoffs. A latent class cluster analysis is shown in Table 3. MayNet is carried out on all 3 data sets for MI, CHF, and for beta thalassemia for comparison and will be shown.

Discussion: CKMB has been heavily used for a long time to determine heart attacks. It is used in conjunction with a troponin test and the EKG to identify MI but, it isn’t as sensitive as is needed. A joint committee of the AmericanCollege of Cardiology and European Society of Cardiology (ACC/ESC) has established the criteria for acute, recent or evolving AMI predicated on a typical increase in troponin in the clinical setting of myocardial ischemia (1), which includes the 99^th percentile of a healthy normal population. The improper selection of a troponin decision value is, however, likely to increase over use of hospital resources. A study by Zarich⁸ showed that using an MI cutoff concentration for TnT from a non-acute coronary syndrome (ACS) reference improves risk stratification, but fails to detect a positive TnT in 11.7% of subjects with an ACS syndrome⁸. The specificity of the test increased from 88.4% to 96.7% with corresponding negative predictive values of 99.7% and 96.2%. Lin et al.⁹ recently reported that the use of low reference cutoffs suggested by the new guidelines results in markedly increased TnI-positive cases overall. Associated with a positive TnI and a negative CKMB, these cases are most likely false positive for MI. Maynet relieves this and the following problem effectively.

Monitoring BNP levels is a new and highly efficient way of diagnosing CHF as well as excluding non-cardiac causes of shortness of breath. Listening to breath sounds is only accurate when the disease is advanced to the stage in which the pumping function of the heart is impaired. The pumping of the heart is impaired when the circulation pressure increases above the osmotic pressure of the blood proteins that keep fluid in the circulation, causing fluid to pass into the lung’s airspaces. Our studies combine the BNP with the EKG measurement of QRS duration to predict whether a patient has a high or low ejection fraction, a measure to stage the severity of CHF.

We also had to integrate the information from the hemogram (RBC, MCV) with the hemoglobin A₂ quantitation (BioRad Variant II) for the diagnosis of beta thalassemia. We chose an approach to the data that requires no assumption about the distribution of test values or the variances. Our detailed analyses validates an approach to thalassemia screening that has been widely used, the Mentzer index¹⁰, and in addition uses critical decision values for the tests that are used in the Mentzer index. We also showed that Hgb S has an effect on both Hgb A2 and Hgb F. This study is adequately powered to assess the usefulness of the Hgb A2 criteria but not adequately powered to assess thalassemias with elevated Hgb F.

References:

1. Adan J, Bernstein LH, Babb J. Lactate dehydrogenase isoenzyme-1/total ratio: accurate for determining the existence of myocardial infarction. Clin Chem 1986;32:624-8.

2. Rudolph RA, Bernstein LH, Babb J. Information induction for predicting acute myocardial infarction. Clin Chem 1988;34:2031- 2038.

3. Magidson J. “Maximum Likelihood Assessment of Clinical Trials Based on an Ordered Categorical Response.” Drug Information Journal, Maple Glen, PA: Drug Information Association 1996;309[1]: 143-170.

4. Magidson J and Vermoent J. Latent Class Cluster Analysis. in J. A. Hagenaars and A. L. McCutcheon (eds.), Applied Latent Class Analysis. Cambridge: CambridgeUniversity Press, 2002, pp. 89-106.

5. Mkhitarian VS, Mayzlin IE, Troshin LI, Borisenko LV. Classification of the base objects upon integral parameters of the attached network. Applied Mathematics and Computers. Moscow, USSR: Statistika, 1976: 118-24.

6.Mayzlin IE, Mkhitarian VS. Determining the optimal bounds for objects of different classes. In: Dubrow AM, ed. Computational Mathematics and Applications. MoscowUSSR: Economics and Statistics Institute. 1976: 102-105.

7. RumelhartDE, Hinton GE, Williams RJ. Learning internal representations by error propagation. In:

RumelhartDE, Mc Clelland JL, eds. Parallel distributed processing. Cambridge, Mass: MIT Press, 1986; 1: 318-62.

8. Zarich SW, Bradley K, Mayall ID, Bernstein, LH. Minor Elevations in Troponin T Values Enhance Risk Assessment in Emergency Department Patients with Suspected Myocardial Ischemia: Analysis of Novel Troponin T Cut-off Values. Clin Chim Acta 2004 (in press).

9. Lin JC, Apple FS, Murakami MM, Luepker RV. Rates of positive cardiac troponin I and creatine kinase MB mass among patients hospitalized for suspected acute coronary syndromes. Clin Chem 2004;50:333-338.

10.Makris PE. Utilization of a new index to distinguish heterozygous thalassemic syndromes: comparison of its specificity to five other discriminants.Blood Cells. 1989;15(3):497-506.

Acknowledgements: Jerard Kneifati-Hayek and Madeleine Schlefer, Midwood High School, Brooklyn, and Salman Haq, Cardiology Fellow, Methodist Hospital.

Table 1. Effect of selection of maximum distance on the number of classes formed and on the accuracy of recognition by ANN

ClusteringDistanceFactor F(D = F * R)

Number ofClasses

Number of Nodes inThe HiddenLayers

Number ofMisrecognizedPatterns inThe TestingSet of 43

Percent ofMisrecognized

10.90.80.7

2414135

1, 02, 03, 01, 02, 03, 0

3, 2

121121

2.34.62.32.34.62.3

2.3

Figure 1.

Figure 2.

Table 2. Frequency cross-table, probabilities and odds-ratios for scaled TnI versus expected diagnosis

Range	Not MI	MI	N	Pct in MI	Prob by TnI	Odds Ratio
< 0.45	655	2	657	2	0	1
0.46-0.6	7	0	7	0	0.03	13
0.61-0.75	4	0	4	0.	0.26	175
0.76-0.9	13	59	72	57.3	0.82	2307
> 0.9	0	42	42	40.8	0.98	30482
	679	103	782	100

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Follow-up on Tomosynthesis

Writer & Curator: Dror Nir, PhD

Integration of 3D digital mammography with tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study

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In order to better link the information given by “scientific” papers to the context of daily patients’ reality I suggest to spend some time reviewing few of the videos in the below links:

The pivotal questions yet to be answered are:

What should be done in order to translate increase in sensitivity and early detection into decrease in mortality?

What is the price of such increase in sensitivity in terms of quality of life and health-care costs and is it worth-while to pay?

Stephen L. Rose1, Andra L. Tidwell1, Louis J. Bujnoch1, Anne C. Kushwaha1, Amy S. Nordmann1 and Russell Sexton, Jr.1

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***Integration of 3D digital mammography with* *tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study***

Stephen L. Rose¹, Andra L. Tidwell¹, Louis J. Bujnoch¹, Anne C. Kushwaha¹, Amy S. Nordmann¹ and Russell Sexton, Jr.¹