Accurate Expression Profiling of Very Small Cell Populations

BACKGROUND: Expression profiling, the measurement of all transcripts of a cell or tissue type, is currently the most comprehensive method to describe their physiological states. Given that accurate profiling methods currently available require RNA amounts found in thousands to millions of cells, many fields of biology working with specialized cell types cannot use these techniques because available cell numbers are limited. Currently available alternative methods for expression profiling from nanograms of RNA or from very small cell populations lack a broad validation of results to provide accurate information about the measured transcripts. METHODS AND FINDINGS: We provide evidence that currently available methods for expression profiling of very small cell populations are prone to technical noise and therefore cannot be used efficiently as discovery tools. Furthermore, we present Pico Profiling, a new expression profiling method from as few as ten cells, and we show that this approach is as informative as standard techniques from thousands to millions of cells. The central component of Pico Profiling is Whole Transcriptome Amplification (WTA), which generates expression profiles that are highly comparable to those produced by others, at different times, by standard protocols or by Real-time PCR. We provide a complete workflow from RNA isolation to analysis of expression profiles. CONCLUSIONS: Pico Profiling, as presented here, allows generating an accurate expression profile from cell populations as small as ten cells

Postoperative complications do not impact on recurrence and survival after curative resection of gastric cancer.

BACKGROUND: We assessed the impact of complications on recurrence and survival after curative gastric cancer resection. METHODS: Patients undergoing R0 resections between 1990 and 2009 were identified in a prospectively maintained database and were categorized by presence of any complication Clavien-Dindo (CD) ≥ II, sepsis or intra-abdominal sepsis. Cox regression analyses to relate complications and clinico-pathological variables to time to recurrence (TTR) and overall survival (OS) were performed. RESULTS: A total of 271 patients were included with a median follow-up of 149.9 months (range 140.1-159.9). complications CD ≥ II occurred in 162 (59.8%) patients, sepsis in 66 (22.5%), and intra-abdominal sepsis in 37 (13.6%). Recurrence developed in 88 (32.4%) patients. Independent predictors of short TTR were pTNM stage (IIIB-IIIC vs. IA-IIA) (hazard ratio [HR] = 37.55, 95% confidence interval [CI] 17.57-80.24; p < 0.001), D1 lymphadenectomy (HR = 3.14, 95% CI 1.94-5.07; p < 0.001), and male gender (HR = 1.65, 95% CI 1.06-2.57; p = 0.026). pTNM stage (IIIB-IIIC vs. IA-IIA, HR = 10.28, 95% CI 6.51-16.23; p < 0.001), male gender (HR = 1.64, 95% CI 1.17-2.31; p = 0.005), age (HR = 1.03, 95% CI 1.02-1.05; p < 0.001), and adjuvant therapy (HR = 0.55, 95% CI 0.37-0.83; p = 0.004) were identified as independent predictors of OS../nCONCLUSIONS: Evidence provided by this study does not support a negative impact of postoperative complications CD ≥ II, sepsis, and intra-abdominal sepsis on the oncologic outcome after curative gastric cancer resection

Enhanced MAF Oncogene Expression and Breast Cancer Bone Metastasis.

BACKGROUND: There are currently no biomarkers for early breast cancer patient populations at risk of bone metastasis. Identification of mediators of bone metastasis could be of clinical interest. METHODS: A de novo unbiased screening approach based on selection of highly bone metastatic breast cancer cells in vivo was used to determine copy number aberrations (CNAs) associated with bone metastasis. The CNAs associated with bone metastasis were examined in independent primary breast cancer datasets with annotated clinical follow-up. The MAF gene encoded within the CNA associated with bone metastasis was subjected to gain and loss of function validation in breast cancer cells (MCF7, T47D, ZR-75, and 4T1), its downstream mechanism validated, and tested in clinical samples. A multivariable Cox cause-specific hazard model with competing events (death) was used to test the association between 16q23 or MAF and bone metastasis. All statistical tests were two-sided. RESULTS: 16q23 gain CNA encoding the transcription factor MAF mediates breast cancer bone metastasis through the control of PTHrP. 16q23 gain (hazard ratio (HR) for bone metastasis = 14.5, 95% confidence interval (CI) = 6.4 to 32.9, P < .001) as well as MAF overexpression (HR for bone metastasis = 2.5, 95% CI = 1.7 to 3.8, P < .001) in primary breast tumors were specifically associated with risk of metastasis to bone but not to other organs. CONCLUSIONS: These results suggest that MAF is a mediator of breast cancer bone metastasis. 16q23 gain or MAF protein overexpression in tumors may help to select patients at risk of bone relapse.MP and SG is supported by “La Caixa” PhD fellowship program. AAE and AB hold PhD fellowships from the Spanish Ministerio de Ciencia e Innovación (MICINN). MTS is supported by the IRB Barcelona PhD program. JU is a “Juan de la Cierva” Researcher (MICINN). FIS PI12/00680, PI12/01552, and PI12/01421 supported JA, ALl, and FR, respectively. JA, ALl, and AP were part of RD12/0036/0051, RD12/0036/0070, and RD12/0036/0042, and FR is part of biobanc RD/09/0076/00101. JA and FR are recipients of intensification grant ISCIII, 2009SGR321, XBTC, MARBiobanc, and Cellex. RRG was supported by the Institució Catalana de Recerca i Estudis Avançats. Support and structural funds were provided by the BBVA Foundation, the Generalitat de Catalunya (2014 SGR 535), and the Spanish Ministerio de Ciencia e Innovación (MICINN) (SAF2013-46196) to RRG

Phosphorylated-insulin growth factor I receptor (p-IGF1R) and metalloproteinase-3 (MMP3) expression in advanced gastrointestinal stromal tumors (GIST). A GEIS 19 study

Background: Most GISTs have mutations in KIT or PDGFRA. Patients with advanced GIST with KIT exon 9, PDGFRA mutation or WT for KIT and PDGFRA have a worse progression‑free survival (PFS) compared to patients with KIT exon 11 mutated tumors. We evaluated the immunohistochemical (IHC) expression of p‑IGF1R (Y1316) and MMP3 as pre‑dictors of PFS or overall survival (OS). Methods: Ninety‑two advanced GIST patients included in GEIS‑16 study with KIT and PDGFRA mutational informa‑tion were examined for p‑IGF1R (Y1316) and MMP3 expression in a tissue micro‑array. To study activation of the IGF1R system, we have used an antibody (anti‑pY1316) that speciically recognizes the active phosphorylated form of the IGF1R. DNA was extracted from parain‑embedded tissues and intronic PCR primers were used to amplify exons 9, 11, 13 and 17 of KIT, 12 and 18 of PDGFRA. Bidirectional sequencing with speciic primers was performed on a ABI3100 sequencer using the Big Dye Terminator v3.1 kit. Multivariate model was built using a stepwise automated variable selection approach with criterion to enter the variable in the model of p < 0.10 and criterion to keep the variable in the model of p < 0.05. PFS was computed as the date of imatinib initiation to progression or death. Overall survival was deined as the time from imatinib initiation to death. Results: Phospho‑IGF1R was expressed only in 9 % (2/22) of cases without KIT mutation. MMP3 expression was detected in 2/5 patients (40 %) with PDGFRA mutation, 1/16 patients (6 %) with WT genotype and 7/71 patients (10 %) of KIT mutant patients. At univariate analysis KIT exon 11/13 mutation had better PFS than patients with exon 9 mutation, PDGFRA mutation or WT genotype (p = 0.021; HR: 0.46; 95 %CI (0.28–0.76). Less than 24 months disease free‑interval (HR 24.2, 95 % CI 10.5–55.8), poor performance status (PS) (HR 6.3, 95 % CI 2.5–15.9), extension of disease; >1 organ (HR 1.89; 95 % CI 1.03–3.4) and genotype analysis (HR 0.57, 95 % CI 0.37–0.97) but not immunophenotype analysis (HR 1.53; 95 % CI 0.76–3.06) were the strongest prognostic factors for PFS in the multivariate analysis. Conclusions: Our results do not support p‑IGF‑1R and MMP3 evaluation in non‑selected GIST patients but evalua‑tion of this immunophenotype in WT and mutant PDGFR mutation in larger group of GIST patients, deserve merits

Enhanced MAF Oncogene Expression and Breast Cancer Bone Metastasis

Background: There are currently no biomarkers for early breast cancer patient populations at risk of bone metastasis. Identification of mediators of bone metastasis could be of clinical interest. Methods: A de novo unbiased screening approach based on selection of highly bone metastatic breast cancer cells in vivo was used to determine copy number aberrations (CNAs) associated with bone metastasis. The CNAs associated with bone metastasis were examined in independent primary breast cancer datasets with annotated clinical follow-up. The MAF gene encoded within the CNA associated with bone metastasis was subjected to gain and loss of function validation in breast cancer cells (MCF7, T47D, ZR-75, and 4T1), its downstream mechanism validated, and tested in clinical samples. A multivariable Cox cause-specific hazard model with competing events (death) was used to test the association between 16q23 or MAF and bone metastasis. All statistical tests were two-sided. Results: 16q23 gain CNA encoding the transcription factor MAF mediates breast cancer bone metastasis through the control of PTHrP. 16q23 gain (hazard ratio (HR) for bone metastasis = 14.5, 95% confidence interval (CI) = 6.4 to 32.9, P < .001) as well as MAF overexpression (HR for bone metastasis = 2.5, 95% CI = 1.7 to 3.8, P < .001) in primary breast tumors were specifically associated with risk of metastasis to bone but not to other organs. Conclusions: These results suggest that MAF is a mediator of breast cancer bone metastasis. 16q23 gain or MAF protein overexpression in tumors may help to select patients at risk of bone relapse

Enhanced MAF Oncogene Expression and Breast Cancer Bone Metastasis.

BACKGROUND: There are currently no biomarkers for early breast cancer patient populations at risk of bone metastasis. Identification of mediators of bone metastasis could be of clinical interest. METHODS: A de novo unbiased screening approach based on selection of highly bone metastatic breast cancer cells in vivo was used to determine copy number aberrations (CNAs) associated with bone metastasis. The CNAs associated with bone metastasis were examined in independent primary breast cancer datasets with annotated clinical follow-up. The MAF gene encoded within the CNA associated with bone metastasis was subjected to gain and loss of function validation in breast cancer cells (MCF7, T47D, ZR-75, and 4T1), its downstream mechanism validated, and tested in clinical samples. A multivariable Cox cause-specific hazard model with competing events (death) was used to test the association between 16q23 or MAF and bone metastasis. All statistical tests were two-sided. RESULTS: 16q23 gain CNA encoding the transcription factor MAF mediates breast cancer bone metastasis through the control of PTHrP. 16q23 gain (hazard ratio (HR) for bone metastasis = 14.5, 95% confidence interval (CI) = 6.4 to 32.9, P < .001) as well as MAF overexpression (HR for bone metastasis = 2.5, 95% CI = 1.7 to 3.8, P < .001) in primary breast tumors were specifically associated with risk of metastasis to bone but not to other organs. CONCLUSIONS: These results suggest that MAF is a mediator of breast cancer bone metastasis. 16q23 gain or MAF protein overexpression in tumors may help to select patients at risk of bone relapse.MP and SG is supported by “La Caixa” PhD fellowship program. AAE and AB hold PhD fellowships from the Spanish Ministerio de Ciencia e Innovación (MICINN). MTS is supported by the IRB Barcelona PhD program. JU is a “Juan de la Cierva” Researcher (MICINN). FIS PI12/00680, PI12/01552, and PI12/01421 supported JA, ALl, and FR, respectively. JA, ALl, and AP were part of RD12/0036/0051, RD12/0036/0070, and RD12/0036/0042, and FR is part of biobanc RD/09/0076/00101. JA and FR are recipients of intensification grant ISCIII, 2009SGR321, XBTC, MARBiobanc, and Cellex. RRG was supported by the Institució Catalana de Recerca i Estudis Avançats. Support and structural funds were provided by the BBVA Foundation, the Generalitat de Catalunya (2014 SGR 535), and the Spanish Ministerio de Ciencia e Innovación (MICINN) (SAF2013-46196) to RRG