Gene trio signatures as molecular markers to predict response to doxorubicin cyclophosphamide neoadjuvant chemotherapy in breast cancerpatients

Abstract

In breast cancer patients submitted to neoadjuvant chemotherapy (4 cycles of doxorubicin and cyclophosphamide, AC), expression of groups of three genes (gene trio signatures) could distinguish responsive from non-responsive tumors, as demonstrated by cDNA microarray profiling in a previous study by our group. In the current study, we determined if the expression of the same genes would retain the predictive strength, when analyzed by a more accessible technique (real-time RT-PCR). We evaluated 28 samples already analyzed by cDNA microarray, as a technical validation procedure, and 14 tumors, as an independent biological validation set. All patients received neoadjuvant chemotherapy (4 AC). Among five trio combinations previously identified, defined by nine genes individually investigated (BZRP, CLPTM1, MTSS1, NOTCH1, NUP210, PRSS11, RPL37A, SMYD2, and XLHSRF-1), the most accurate were established by RPL37A, XLHSRF-1 based trios, with NOTCH1 or NUP210. Both trios correctly separated 86% of tumors (87% sensitivity and 80% specificity for predicting response), according to their response to chemotherapy (82% in a leave-one-out cross-validation method). Using the pre-established features obtained by linear discriminant analysis, 71% samples from the biological validation set were also correctly classified by both trios (72% sensitivity; 66% specificity). Furthermore, we explored other gene combinations to achieve a higher accuracy in the technical validation group (as a training set). A new trio, MTSS1, RPL37 and SMYD2, correctly classified 93% of samples from the technical validation group (95% sensitivity and 80% specificity; 86% accuracy by the cross-validation method) and 79% from the biological validation group (72% sensitivity and 100% specificity). Therefore, the combined expression of MTSS1, RPL37 and SMYD2, as evaluated by real-time RT-PCR, is a potential candidate to predict response to neoadjuvant doxorubicin and cyclophosphamide in breast cancer patients.

Selección supervisada de polimorfismos de nucleótido único en el síndrome de fatiga crónica

“…This offers the chance to squeeze the maximum out of a small dataset and to procure as accurate an estimate as possible (15) such as in the cases of using molecular markers to predict the response cancer patients will have to a treatment (26) or of the genes involved in the pluripotency of stem cells (27).…”

The effects of telomere shortening on cancer cells: A network model of proteomic and microRNA analysis

“…This overlap is statistically significant (P = 0.006; see Materials and Methods). The same comparison to the groups of proteins related to the sensitivity to doxorubicin [33][34][35][36][37] or vincristine [38][39][40][41]62] did not reveal a significant overlap (the proteins that are related to doxorubicin sensitivity are listed in Table S4). These results are in line with our previous data showing that sensitivity to cisplatin (and not other drugs such as vincristine or doxorubicin) is a typical characteristic of a telomere shortened phenotype of cancer cells [9].…”

An update on chemotherapy and tumor gene expression profiles in breast cancer

“…Since the last review [46], there have been at least 14 more publications in this area of study, resulting in a total of 28 studies that are included in this present review [23,[29][30][31][47][48][49][50][51][52][53][54][55][56]. These studies were all conducted in the neoadjuvant setting, and generally did not have pre-specifications of breast cancer subtypes, although studies in relation to specific subclasses of breast cancer has gained increasing interest, with three studies evaluating specific and more aggressive subtypes like basallike or triple negative cancers [51,31,57].…”

“…Core needle biopsies were the sampling mode in most of the described studies, but it was notable that FNAs yielded sufficient RNA (1 --2 µg) for gene signatures in several studies [49,50,[57][58][59]. While pCR was used as the response end point in most studies, some other response end points were utilized by various investigators, including an arbitrary 25% cutoff [64], WHO or RECIST criteria [18,48,54,65], persistence of axillary lymph nodes as a marker of resistant disease [66] or pathological grading of residual cancer burden [49]. Certain studies allowed near pCR to be combined with pCR in the analysis [16], while others were more stringent [51].…”

“…The size of the predictive gene panel ranged from about 20 to 100 genes, although some consisted of only 3 --9 [18,48,54,60], and others consisted of > 250 genes [30,61,63]. Predictive accuracy ranged from 60 to 100% [11,18,29,31,[47][48][49]54,58,60,61,[63][64][65], and positive and negative predictive values were about 9 --40% and 70 --93%, respectively in the studies reviewed by the authors [23,50,56].…”