Identification of a biomarker profile associated with resistance to neoadjuvant chemoradiation therapy in rectal cancer

OBJECTIVE: To identify a biomarker profile associated with tumor response to chemoradiation (CRT) in locally advanced rectal cancer. BACKGROUND: Rectal cancer response to neoadjuvant CRT is variable. Whereas some patients have a minimal response, others achieve a pathologic complete response (pCR) and have no viable cancer cells in their surgical specimens. Identifying biomarkers of response will help select patients more likely to benefit from CRT. METHODS: This study includes 132 patients with locally advanced rectal cancer treated with neoadjuvant CRT followed by surgery. Tumor DNA from pretreatment tumor biopsies and control DNA from paired normal surgical specimens was screened for mutations and polymorphisms in 23 genes. Genetic biomarkers were correlated with tumor response to CRT (pCR vs non-pCR), and the association of single or combined biomarkers with tumor response was determined. RESULTS: Thirty-three of 132 (25%) patients achieved a pCR and 99 (75%) patients had non-pCR. Three individual markers were associated with non-pCR; v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog mutation (P = 0.0145), cyclin D1 G870A (AA) polymorphism (P = 0.0138), and methylenetetrahydrofolate reductase (NAD(P)H) C677T (TT) polymorphism (P = 0.0120). Analysis of biomarker combinations revealed that none of the 27 patients with both tumor protein p53 (p53) and v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog mutations had a pCR. Further, in patients with both p53 and v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog mutations or the cyclin D1 G870A (AA) polymorphism or the methylenetetrahydrofolate reductase (NAD(P)H) C677T (TT) polymorphism (n = 52) the association with non-pCR was further strengthened; 51 of 52 (98%) of patients were non-pCR. These biomarker combinations had a validity of more than 70% and a positive predictive value of 97% to 100%, predicting that patients harboring these mutation/polymorphism profiles will not achieve a pCR. CONCLUSIONS: A specific biomarker profile is strongly associated with non-pCR to CRT and could be used to select optimal oncologic therapy in rectal cancer patients. ClinicalTrials.org Identifier: NCT00335816

How to Choose the Right Inhaler Using a Patient-Centric Approach?

There are many different inhaler devices and medications on the market for the treatment of asthma and chronic obstructive pulmonary disease, with over 230 drug-delivery system combinations available. However, despite the abundance of effective treatment options, the achieved disease control in clinical practice often remains unsatisfactory. In this context, a key determining factor is the match or mismatch of an inhalation device with the characteristics or needs of an individual patient. Indeed, to date, no ideal device exists that fits all patients, and a personalized approach needs to be considered. Several useful choice-guiding algorithms have been developed in the recent years to improve inhaler-patient matching, but a comprehensive tool that translates the multifactorial complexity of inhalation therapy into a user-friendly algorithm is still lacking. To address this, a multidisciplinary expert panel has developed an evidence-based practical treatment tool that allows a straightforward way of choosing the right inhaler for each patient

Growth Differentiation Factor 9 (GDF9) Suppresses Follistatin and Follistatin-Like 3 Production in Human Granulosa-Lutein Cells

We have demonstrated that growth differentiation factor 9 (GDF9) enhances activin A-induced inhibin β(B)-subunit mRNA levels in human granulosa-lutein (hGL) cells by regulating receptors and key intracellular components of the activin signaling pathway. However, we could not exclude its effects on follistatin (FST) and follistatin-like 3 (FSTL3), well recognized extracellular inhibitors of activin A.hGL cells from women undergoing in vitro fertilization (IVF) treatment were cultured with and without siRNA transfection of FST, FSTL3 or GDF9 and then treated with GDF9, activin A, FST, FSTL3 or combinations. FST, FSTL3 and inhibin β(B)-subunit mRNA, and FST, FSTL3 and inhibin B protein levels were assessed with real-time RT-PCR and ELISA, respectively. Data were log transformed before ANOVA followed by Tukey's test.GDF9 suppressed basal FST and FSTL3 mRNA and protein levels in a time- and dose-dependent manner and inhibited activin A-induced FST and FSTL3 mRNA and protein expression, effects attenuated by BMPR2 extracellular domain (BMPR2 ECD), a GDF9 antagonist. After GDF9 siRNA transfection, basal and activin A-induced FST and FSTL3 mRNA and protein levels increased, but changes were reversed by adding GDF9. Reduced endogenous FST or FSTL3 expression with corresponding siRNA transfection augmented activin A-induced inhibin β(B)-subunit mRNA levels as well as inhibin B levels (P values all <0.05). Furthermore, the enhancing effects of GDF9 in activin A-induced inhibin β(B)-subunit mRNA and inhibin B production were attenuated by adding FST.GDF9 decreases basal and activin A-induced FST and FSTL3 expression, and this explains, in part, its enhancing effects on activin A-induced inhibin β(B)-subunit mRNA expression and inhibin B production in hGL cells

Comparison of immunohistochemistry with PCR for assessment of ER, PR, and Ki-67 and prediction of pathological complete response in breast cancer

Background: Proliferation may predict response to neoadjuvant therapy of breast cancer and is commonly assessed by manual scoring of slides stained by immunohistochemistry (IHC) for Ki-67 similar to ER and PgR. This method carries significant intra- and inter-observer variability. Automatic scoring of Ki-67 with digital image analysis (qIHC) or assessment of MKI67 gene expression with RT-qPCR may improve diagnostic accuracy. Methods: Ki-67 IHC visual assessment was compared to the IHC nuclear tool (AperioTM) on core biopsies from a randomized neoadjuvant clinical trial. Expression of ESR1, PGR and MKI67 by RT-qPCR was performed on RNA extracted from the same formalin-fixed paraffin-embedded tissue. Concordance between the three methods (vIHC, qIHC and RT-qPCR) was assessed for all 3 markers. The potential of Ki-67 IHC and RT-qPCR to predict pathological complete response (pCR) was evaluated using ROC analysis and non-parametric Mann-Whitney Test. Results: Correlation between methods (qIHC versus RT-qPCR) was high for ER and PgR (spearman´s r = 0.82, p < 0.0001 and r = 0.86, p < 0.0001, respectively) resulting in high levels of concordance using predefined cut-offs. When comparing qIHC of ER and PgR with RT-qPCR of ESR1 and PGR the overall agreement was 96.6 and 91.4%, respectively, while overall agreement of visual IHC with RT-qPCR was slightly lower for ER/ESR1 and PR/PGR (91.2 and 92.9%, respectively). In contrast, only a moderate correlation was observed between qIHC and RT-qPCR continuous data for Ki-67/MKI67 (Spearman’s r = 0.50, p = 0.0001). Up to now no predictive cut-off for Ki-67 assessment by IHC has been established to predict response to neoadjuvant chemotherapy. Setting the desired sensitivity at 100%, specificity for the prediction of pCR (ypT0ypN0) was significantly higher for mRNA than for protein (68.9% vs. 22.2%). Moreover, the proliferation levels in patients achieving a pCR versus not differed significantly using MKI67 RNA expression (Mann-Whitney p = 0.002), but not with qIHC of Ki-67 (Mann-Whitney p = 0.097) or vIHC of Ki-67 (p = 0.131). Conclusion: Digital image analysis can successfully be implemented for assessing ER, PR and Ki-67. IHC for ER and PR reveals high concordance with RT-qPCR. However, RT-qPCR displays a broader dynamic range and higher sensitivity than IHC. Moreover, correlation between Ki-67 qIHC and RT-qPCR is only moderate and RT-qPCR with MammaTyper® outperforms qIHC in predicting pCR. Both methods yield improvements to error-prone manual scoring of Ki-67. However, RT-qPCR was significantly more specific

Harnessing citizen science through mobile phone technology to screen for immunohistochemical biomarkers in bladder cancer

Background: Immunohistochemistry (IHC) is often used in personalisation of cancer treatments. Analysis of large data sets to uncover predictive biomarkers by specialists can be enormously time-consuming. Here we investigated crowdsourcing as a means of reliably analysing immunostained cancer samples to discover biomarkers predictive of cancer survival. Methods: We crowdsourced the analysis of bladder cancer TMA core samples through the smartphone app ‘Reverse the Odds’. Scores from members of the public were pooled and compared to a gold standard set scored by appropriate specialists. We also used crowdsourced scores to assess associations with disease-specific survival. Results: Data were collected over 721 days, with 4,744,339 classifications performed. The average time per classification was approximately 15 s, with approximately 20,000 h total non-gaming time contributed. The correlation between crowdsourced and expert H-scores (staining intensity × proportion) varied from 0.65 to 0.92 across the markers tested, with six of 10 correlation coefficients at least 0.80. At least two markers (MRE11 and CK20) were significantly associated with survival in patients with bladder cancer, and a further three markers showed results warranting expert follow-up. Conclusions: Crowdsourcing through a smartphone app has the potential to accurately screen IHC data and greatly increase the speed of biomarker discovery