Validation Between Gene Expression Measurement Platforms

https://openworks.mdanderson.org/sumexp22/1088/thumbnail.jp

Lack of sufficiently strong informative features limits the potential of gene expression analysis as predictive tool for many clinical classification problems

<p>Abstract</p> <p>Background</p> <p>Our goal was to examine how various aspects of a gene signature influence the success of developing multi-gene prediction models. We inserted gene signatures into three real data sets by altering the expression level of existing probe sets. We varied the number of probe sets perturbed (signature size), the fold increase of mean probe set expression in perturbed compared to unperturbed data (signature strength) and the number of samples perturbed. Prediction models were trained to identify which cases had been perturbed. Performance was estimated using Monte-Carlo cross validation.</p> <p>Results</p> <p>Signature strength had the greatest influence on predictor performance. It was possible to develop almost perfect predictors with as few as 10 features if the fold difference in mean expression values were > 2 even when the spiked samples represented 10% of all samples. We also assessed the gene signature set size and strength for 9 real clinical prediction problems in six different breast cancer data sets.</p> <p>Conclusions</p> <p>We found sufficiently large and strong predictive signatures only for distinguishing ER-positive from ER-negative cancers, there were no strong signatures for more subtle prediction problems. Current statistical methods efficiently identify highly informative features in gene expression data if such features exist and accurate models can be built with as few as 10 highly informative features. Features can be considered highly informative if at least 2-fold expression difference exists between comparison groups but such features do not appear to be common for many clinically relevant prediction problems in human data sets.</p

DNA repair deficiency biomarkers and the 70-gene ultra-high risk signature as predictors of veliparib/carboplatin response in the I-SPY 2 breast cancer trial.

Veliparib combined with carboplatin (VC) was an experimental regimen evaluated in the biomarker-rich neoadjuvant I-SPY 2 trial for breast cancer. VC showed improved efficacy in the triple negative signature. However, not all triple negative patients achieved pathologic complete response and some HR+HER2- patients responded. Pre-specified analysis of five DNA repair deficiency biomarkers (BRCA1/2 germline mutation; PARPi-7, BRCA1ness, and CIN70 expression signatures; and PARP1 protein) was performed on 116 HER2- patients (VC: 72 and concurrent controls: 44). We also evaluated the 70-gene ultra-high risk signature (MP1/2), one of the biomarkers used to define subtype in the trial. We used logistic modeling to assess biomarker performance. Successful biomarkers were combined using a simple voting scheme to refine the 'predicted sensitive' group and Bayesian modeling used to estimate the pathologic complete response rates. BRCA1/2 germline mutation status associated with VC response, but its low prevalence precluded further evaluation. PARPi-7, BRCA1ness, and MP1/2 specifically associated with response in the VC arm but not the control arm. Neither CIN70 nor PARP1 protein specifically predicted VC response. When we combined the PARPi-7 and MP1/2 classifications, the 42% of triple negative patients who were PARPi7-high and MP2 had an estimated pCR rate of 75% in the VC arm. Only 11% of HR+/HER2- patients were PARPi7-high and MP2; but these patients were also more responsive to VC with estimated pathologic complete response rates of 41%. PARPi-7, BRCA1ness and MP1/2 signatures may help refine predictions of VC response, thereby improving patient care

Killed in Cold Blood: An exploration of the efficacy of oncolytic adenoviruses in metastatic breast cancer

https://openworks.mdanderson.org/sumexp21/1182/thumbnail.jp

Neoadjuvant Trastuzumab Emtansine and Pertuzumab in Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer: Three-Year Outcomes From the Phase III KRISTINE Study.

PurposeThe KRISTINE study compared neoadjuvant trastuzumab emtansine plus pertuzumab (T-DM1+P) with docetaxel, carboplatin, trastuzumab plus P (TCH+P) for the treatment human epidermal growth factor receptor 2-positive stage II to III breast cancer. T-DM1+P led to a lower pathologic complete response rate (44.4% v 55.7%; P = .016), but fewer grade 3 or greater and serious adverse events (AEs). Here, we present 3-year outcomes from KRISTINE.MethodsPatients were randomly assigned to neoadjuvant T-DM1+P or TCH+P every 3 weeks for six cycles. Patients who received T-DM1+P continued adjuvant T-DM1+P, and patients who received TCH+P received adjuvant trastuzumab plus pertuzumab. Secondary end points included event-free survival (EFS), overall survival, patient-reported outcomes (measured from random assignment), and invasive disease-free survival (IDFS; measured from surgery).ResultsOf patients, 444 were randomly assigned (T-DM1+P, n = 223; TCH+P, n = 221). Median follow-up was 37 months. Risk of an EFS event was higher with TDM-1+P (hazard ratio [HR], 2.61 [95% CI, 1.36 to 4.98]) with more locoregional progression events before surgery (15 [6.7%] v 0). Risk of an IDFS event after surgery was similar between arms (HR, 1.11 [95% CI, 0.52 to 2.40]). Pathologic complete response was associated with a reduced risk of an IDFS event (HR, 0.24 [95% CI, 0.09 to 0.60]) regardless of treatment arm. Overall, grade 3 or greater AEs (31.8% v 67.7%) were less common with T-DM1+P. During adjuvant treatment, grade 3 or greater AEs (24.5% v 9.9%) and AEs leading to treatment discontinuation (18.4% v 3.8%) were more common with T-DM1+P. Patient-reported outcomes favored T-DM1+P during neoadjuvant treatment and were similar to trastuzumab plus pertuzumab during adjuvant treatment.ConclusionCompared with TCH+P, T-DM1+P resulted in a higher risk of an EFS event owing to locoregional progression events before surgery, a similar risk of an IDFS event, fewer grade 3 or greater AEs during neoadjuvant treatment, and more AEs leading to treatment discontinuation during adjuvant treatment

Maximum predictive power of the microarray-based models for clinical outcomes is limited by correlation between endpoint and gene expression profile

<p>Abstract</p> <p>Background</p> <p>Microarray data have been used for gene signature selection to predict clinical outcomes. Many studies have attempted to identify factors that affect models' performance with only little success. Fine-tuning of model parameters and optimizing each step of the modeling process often results in over-fitting problems without improving performance.</p> <p>Results</p> <p>We propose a quantitative measurement, termed consistency degree, to detect the correlation between disease endpoint and gene expression profile. Different endpoints were shown to have different consistency degrees to gene expression profiles. The validity of this measurement to estimate the consistency was tested with significance at a p-value less than 2.2e-16 for all of the studied endpoints. According to the consistency degree score, overall survival milestone outcome of multiple myeloma was proposed to extend from 730 days to 1561 days, which is more consistent with gene expression profile.</p> <p>Conclusion</p> <p>For various clinical endpoints, the maximum predictive powers of different microarray-based models are limited by the correlation between endpoint and gene expression profile of disease samples as indicated by the consistency degree score. In addition, previous defined clinical outcomes can also be reassessed and refined more coherent according to related disease gene expression profile. Our findings point to an entirely new direction for assessing the microarray-based predictive models and provide important information to gene signature based clinical applications.</p

Proliferation and estrogen signaling can distinguish patients at risk for early versus late relapse among estrogen receptor positive breast cancers

Introduction: We examined if a combination of proliferation markers and estrogen receptor (ER) activity could predict early versus late relapses in ER-positive breast cancer and inform the choice and length of adjuvant endocrine therapy. Methods: Baseline affymetrix gene-expression profiles from ER-positive patients who received no systemic therapy (n = 559), adjuvant tamoxifen for 5 years (cohort-1: n = 683, cohort-2: n = 282) and from 58 patients treated with neoadjuvant letrozole for 3 months (gene-expression available at baseline, 14 and 90 days) were analyzed. A proliferation score based on the expression of mitotic kinases (MKS) and an ER-related score (ERS) adopted from Oncotype DX® were calculated. The same analysis was performed using the Genomic Grade Index as proliferation marker and the luminal gene score from the PAM50 classifier as measure of estrogen-related genes. Median values were used to define low and high marker groups and four combinations were created. Relapses were grouped into time cohorts of 0-2.5, 0-5, 5-10 years. Results: In the overall 10 years period, the proportional hazards assumption was violated for several biomarker groups indicating time-dependent effects. In tamoxifen-treated patients Low-MKS/Low-ERS cancers had continuously increasing risk of relapse that was higher after 5 years than Low-MKS/High-ERS cancers [0 to 10 year, HR 3.36; p = 0.013]. High-MKS/High-ERS cancers had low risk of early relapse [0-2.5 years HR 0.13; p = 0.0006], but high risk of late relapse which was higher than in the High-MKS/Low-ERS group [after 5 years HR 3.86; p = 0.007]. The High-MKS/Low-ERS subset had most of the early relapses [0 to 2.5 years, HR 6.53; p < 0.0001] especially in node negative tumors and showed minimal response to neoadjuvant letrozole. These findings were qualitatively confirmed in a smaller independent cohort of tamoxifen-treated patients. Using different biomarkers provided similar results. Conclusions: Early relapses are highest in highly proliferative/low-ERS cancers, in particular in node negative tumors. Relapses occurring after 5 years of adjuvant tamoxifen are highest among the highly-proliferative/high-ERS tumors although their risk of recurrence is modest in the first 5 years on tamoxifen. These tumors could be the best candidates for extended endocrine therapy