Comparison of prognostic genomic predictors in colorectal cancer

Enhancing chemotherapy response prediction via matched colorectal tumor-organoid gene expression analysis and network-based biomarker selection

BACKGROUND

Colorectal cancer (CRC) presents significant challenges in chemotherapy response prediction due to its molecular heterogeneity. Current methods often fail to account for the complexity and variability inherent in individual tumors.

METHODS

We developed a novel approach using matched CRC tumor and organoid gene expression data. We applied Consensus Weighted Gene Co-expression Network Analysis (WGCNA) across three datasets: CRC tumors, matched organoids, and an independent organoid dataset with IC50 drug response values, to identify key gene modules and hub genes linked to chemotherapy response, particularly 5-fluorouracil (5-FU).

FINDINGS

Our integrative analysis identified significant gene modules and hub genes associated with CRC chemotherapy response. The predictive model built from these findings demonstrated superior accuracy over traditional methods when tested on independent datasets. The matched tumor-organoid data approach proved effective in capturing relevant biomarkers, enhancing prediction reliability.

INTERPRETATION

This study provides a robust framework for improving CRC chemotherapy response predictions by leveraging matched tumor and organoid gene expression data. Our approach addresses the limitations of previous methods, offering a promising strategy for personalized treatment planning in CRC. Future research should aim to validate these findings and explore the integration of more comprehensive drug response data.

FUNDING

This research was supported by US National Cancer Institute grant R37CA248289, and Sylvester Comprehensive Cancer Center. which receives funding from the National Cancer Institute award P30CA240139. This work was supported by National Institutes of Health (NIH) under the following grants: T32CA009501-31A1 and R37CA248289. This work was also supported by the MSK P30CA008748 grant.

High expression of PAX8-AS1 correlates with poor prognosis and response to fluorouracil-based chemotherapy in stage II colon cancer

Decisions regarding adjuvant therapy in patients with stage II colon cancer remains controversial and challenging. We aimed to determine novel biomarkers that help to predict relapse free survival (RFS) and identify a subset of patients with stage II colon cancer who could gain survival benefits from adjuvant chemotherapy. Public microarray datasets of stage II colon cancer samples were extracted from Gene Expression Omnibus database. Global gene expression changes were then analyzed between the paired early relapse and long-term survival group to identify the differentially expressed mRNAs and lncRNAs. Based on Lasso Cox regression modeling analysis, a total of 30 mRNAs and 2 lncRNAs were finally identified. With specific formula, stage II patients in training and validation sets were divided into low and risk groups with significantly different RFS. PAX8-AS1 is the novel lncRNA which showed the highest upregulation in early relapse group. Patients with high PAX8-AS1 expression level showed notably poorer RFS in both meta GEO cohort (P = 0.04, Figure 4B) and FUSCC cohort (P < 0.001, Figure 4C). Among the stage II patients with high PAX8-AS1 level, administration of fluorouracil-based adjuvant chemotherapy provided a substantial improvement in RFS (P = 0.002, Figure 3C). Further mechanistic study unveiled that PAX8-AS1 increases the response of CRC cells to chemotherapy in vitro and in vivo by maintaining the mRNA stability of PAX8. In conclusion, PAX8-AS1 as a novel and reliable biomarker for predicting prognosis and identification of patients with stage II disease who could gain survival benefit from fluorouracil-based adjuvant chemotherapy.

Discovery and validation of a 10-gene predictive signature for response to adjuvant chemotherapy in stage II and III colon cancer

Identifying patients with stage II and III colon cancer who will benefit from 5-fluorouracil (5-FU)-based adjuvant chemotherapy is crucial for the advancement of personalized cancer therapy. We employ a semi-supervised machine learning approach to analyze a large dataset with 933 stage II and III colon cancer samples. Our analysis leverages gene regulatory networks to discover an 18-gene prognostic signature and to explore a 10-gene signature that potentially predicts chemotherapy benefits. The 10-gene signature demonstrates strong prognostic power and shows promising potential to predict chemotherapy benefits. We establish a robust clinical assay on the NanoString nCounter platform, validated in a retrospective formalin-fixed paraffin-embedded (FFPE) cohort, which represents an important step toward clinical application. Our study lays the groundwork for improving adjuvant chemotherapy and potentially expanding into immunotherapy decision-making in colon cancer. Future prospective studies are needed to validate and establish the clinical utility of the 10-gene signature in clinical settings.

Enhancing Chemotherapy Response Prediction via Matched Colorectal Tumor-Organoid Gene Expression Analysis and Network-Based Biomarker Selection

Colorectal cancer (CRC) poses significant challenges in chemotherapy response prediction due to its molecular heterogeneity. This study introduces an innovative methodology that leverages gene expression data generated from matched colorectal tumor and organoid samples to enhance prediction accuracy. By applying Consensus Weighted Gene Co-expression Network Analysis (WGCNA) across multiple datasets, we identify critical gene modules and hub genes that correlate with patient responses, particularly to 5-fluorouracil (5-FU). This integrative approach advances precision medicine by refining chemotherapy regimen selection based on individual tumor profiles. Our predictive model demonstrates superior accuracy over traditional methods on independent datasets, illustrating significant potential in addressing the complexities of high-dimensional genomic data for cancer biomarker research.

KRT17 as a prognostic biomarker for stage II colorectal cancer

Adjuvant chemotherapy is considered for patients with stage II colorectal cancer (CRC) characterized by poor prognostic clinicopathological features; however, current stratification algorithms remain inadequate for identifying high-risk patients. To develop prognostic assays, we conducted a step-wise screening and validation strategy using nine cohorts of stage II patients based on multiple platforms, including microarray, RNA-sequencing (RNA-seq) and immunohistochemistry (IHC) on formalin-fixed paraffin-embedded (FFPE) tissues. Four microarray datasets (total n = 458) were used as the discovery set to screen for single genes associated with postoperative recurrence. Prognostic values of candidate genes were evaluated in three independent microarray/RNA-seq validation cohorts (n = 89, n = 93 and n = 183, respectively), and then IHC for KRT17 was conducted in two independent FFPE series (n = 110 and n = 44, respectively). We found that high levels of KRT17 transcript expression were significantly associated with poor relapse-free survival (RFS) not only in the discovery set, but also in three validation cohorts, and its prognostic impact was independent of conventional factors by multivariate analyses. Positive staining of KRT17 protein was significantly associated with poor RFS in two independent FFPE cohorts. KRT17 protein expression had independent prognostic impact on RFS in a multivariate model adjusted for conventional variables, including high-risk clinicopathological features. In conclusion, using nine independent cohorts consisting of 997 stage II patients, we identified and validated the expression of KRT17 transcript and KRT17 protein as a robust prognostic biomarker that can discriminate postoperative stage II patients who are at high probability of disease recurrence, providing additional prognostic stratification beyond the currently available high-risk factors.

Prognostic and Predictive Molecular Biomarkers for Colorectal Cancer: Updates and Challenges

Colorectal cancer (CRC) is a leading cause of death among cancer patients. This heterogeneous disease is characterized by alterations in multiple molecular pathways throughout its development. Mutations in RAS, along with the mismatch repair gene deficiency, are currently routinely tested in clinics. Such biomarkers provide information for patient risk stratification and for the choice of the best treatment options. Nevertheless, reliable and powerful prognostic markers that can identify “high-risk” CRC patients, who might benefit from adjuvant chemotherapy, in early stages, are currently missing. To bridge this gap, genomic information has increasingly gained interest as a potential method for determining the risk of recurrence. However, due to several limitations of gene-based signatures, these have not yet been clinically implemented. In this review, we describe the different molecular markers in clinical use for CRC, highlight new markers that might become indispensable over the next years, discuss recently developed gene expression-based tests and highlight the challenges in biomarker research.

Cancer stem cell associated eight gene-based signature predicts clinical outcomes of colorectal cancer

Previous studies have suggested that cancer stem cells serve crucial functions in tumorigenesis, metastasis and therapy failure. Stem cell signaling transduction pathways are frequently dysregulated in cancer and associated with tumorigenesis, metastasis and the cell cycle, which are necessary for cancer proliferation. However, cancer stem cell-associated gene signatures have not been established for predicting patient outcomes in colorectal cancer. Using a gene-mining approach, the present study performed mRNA expression profiling in large colorectal cancer cohorts from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, including a TCGA colorectal cancer cohort (n=383) and three independent validation series GSE39582 (n=582), GSE17536 (n=177) and GSE17537 (n=55). The present study identified that an eight-gene signature in cancer stem cell signaling was associated with the overall survival and disease/recurrence-free survival of patients with colorectal. On the basis of this signature, patients in the TCGA training sets were divided into high-risk and low-risk subgroups with a significantly different overall survival rate (hazard ratio, 2.38; P=0.0005). The prognostic value of this signature was confirmed using three independent GEO colorectal cancer sets. Identifying this prognostic stem cell signaling signature may provide an efficient classification tool for clinical prognosis evaluation, and facilitate cancer stem cell-targeted therapy.

Genome-wide Discovery and Identification of a Novel miRNA Signature for Recurrence Prediction in Stage II and III Colorectal Cancer

Purpose: The current tumor-node-metastasis (TNM) staging system is inadequate at identifying patients with high-risk colorectal cancer. Using a systematic and comprehensive biomarker discovery and validation approach, we aimed to identify an miRNA recurrence classifier (MRC) that can improve upon the current TNM staging as well as is superior to currently offered molecular assays.Experimental Design: Three independent genome-wide miRNA expression profiling datasets were used for biomarker discovery (N = 158) and in silico validation (N = 109 and N = 40) to identify an miRNA signature for predicting tumor recurrence in patients with colorectal cancer. Subsequently, this signature was analytically trained and validated in retrospectively collected independent patient cohorts of fresh-frozen (N = 127, cohort 1) and formalin-fixed paraffin-embedded (FFPE; N = 165, cohort 2 and N = 139, cohort 3) specimens.Results: We identified an 8-miRNA signature that significantly predicted recurrence-free interval (RFI) in the discovery (P = 0.002) and two independent publicly available datasets (P = 0.00006 and P = 0.002). The RT-PCR-based validation in independent clinical cohorts revealed that MRC-derived high-risk patients succumb to significantly poor RFI in patients with stage II and III colorectal cancer [cohort 1: hazard ratio (HR), 3.44 (1.56-7.45), P = 0.001; cohort 2: HR, 6.15 (3.33-11.35), P = 0.001; and cohort 3: HR, 4.23 (2.26-7.92), P = 0.0003]. In multivariate analyses, MRC emerged as an independent predictor of tumor recurrence and achieved superior predictive accuracy over the currently available molecular assays. The RT-PCR-based MRC risk score = (-0.1218 × miR-744) + (-3.7142 × miR-429) + (-2.2051 × miR-362) + (3.0564 × miR-200b) + (2.4997 × miR-191) + (-0.0065 × miR-30c2) + (2.2224 × miR-30b) + (-1.1162 × miR-33a).Conclusions: This novel MRC is superior to currently used clinicopathologic features, as well as National Comprehensive Cancer Network (NCCN) criteria, and works regardless of adjuvant chemotherapy status in identifying patients with high-risk stage II and III colorectal cancer. This can be readily deployed in clinical practice with FFPE specimens for decision-making pending further model testing and validation. Clin Cancer Res; 24(16); 3867-77. ©2018 AACRSee related commentary by Rodriguez et al., p. 3787.

BCL-2 system analysis identifies high-risk colorectal cancer patients

OBJECTIVE

The mitochondrial apoptosis pathway is controlled by an interaction of multiple BCL-2 family proteins, and plays a key role in tumour progression and therapy responses. We assessed the prognostic potential of an experimentally validated, mathematical model of BCL-2 protein interactions (DR_MOMP) in patients with stage III colorectal cancer (CRC).

DESIGN

Absolute protein levels of BCL-2 family proteins were determined in primary CRC tumours collected from n=128 resected and chemotherapy-treated patients with stage III CRC. We applied DR_MOMP to categorise patients as high or low risk based on model outputs, and compared model outputs with known prognostic factors (T-stage, N-stage, lymphovascular invasion). DR_MOMP signatures were validated on protein of n=156 patients with CRC from the Cancer Genome Atlas (TCGA) project.

RESULTS

High-risk stage III patients identified by DR_MOMP had an approximately fivefold increased risk of death compared with patients identified as low risk (HR 5.2, 95% CI 1.4 to 17.9, p=0.02). The DR_MOMP signature ranked highest among all molecular and pathological features analysed. The prognostic signature was validated in the TCGA colon adenocarcinoma (COAD) cohort (HR 4.2, 95% CI 1.1 to 15.6, p=0.04). DR_MOMP also further stratified patients identified by supervised gene expression risk scores into low-risk and high-risk categories. BCL-2-dependent signalling critically contributed to treatment responses in consensus molecular subtypes 1 and 3, linking for the first time specific molecular subtypes to apoptosis signalling.

CONCLUSIONS

DR_MOMP delivers a system-based biomarker with significant potential as a prognostic tool for stage III CRC that significantly improves established histopathological risk factors.

Colon cancer subtypes: concordance, effect on survival and selection of the most representative preclinical models

Multiple gene-expression-based subtypes have been proposed for the molecular subdivision of colon cancer in the last decade. We aimed to cross-validate these classifiers to explore their concordance and their power to predict survival. A gene-chip-based database comprising 2,166 samples from 12 independent datasets was set up. A total of 22 different molecular subtypes were re-trained including the CCHS, CIN25, CMS, ColoGuideEx, ColoGuidePro, CRCassigner, MDA114, Meta163, ODXcolon, Oncodefender, TCA19, and V7RHS classifiers as well as subtypes established by Budinska, Chang, DeSousa, Marisa, Merlos, Popovici, Schetter, Yuen, and Watanabe (first authors). Correlation with survival was assessed by Cox proportional hazards regression for each classifier using relapse-free survival data. The highest efficacy at predicting survival in stage 2-3 patients was achieved by Yuen (p = 3.9e-05, HR = 2.9), Marisa (p = 2.6e-05, HR = 2.6) and Chang (p = 9e-09, HR = 2.35). Finally, 61 colon cancer cell lines from four independent studies were assigned to the closest molecular subtype.

A systematic review of clinically available gene expression profiling assays for stage II colorectal cancer: initial steps toward genetic staging

BACKGROUND

In recent years, increasingly accessible and novel genetic technologies have spurred keen interest in the application of cancer genetics in predicting prognosis and response to treatment. In particular, investigators have eagerly sought to establish and validate genetic signatures that might improve the identification of patients with stage II colorectal cancer (CRC) who are at highest risk of recurrence. To better understand the evidence for incorporation of genetic assays into clinical practice, we have systematically reviewed those assays that have been validated and are available for clinical use in stage II CRC.

METHODS

A systematic review was performed using PubMed, Web of Science and Scopus databases. The GRADE system was used to evaluate level of evidence and strength of recommendations.

RESULTS

After duplicates were removed and exclusion criteria were applied, there were 13 articles for review.

CONCLUSION

Identifying high-risk patients with stage II CRC using molecular profiling has been the primary aim of many investigators, and the approach is translating into clinical utility.

Gene signatures of estrogen and progesterone receptor pathways predict the prognosis of colorectal cancer

The associations of estrogen receptor (ER) and progesterone receptor (PR) pathways with the prognosis of colorectal cancer (CRC) are still controversial. The aim of this study was to readdress these issues by introducing a gene signature-based approach to semiquantitate pathway activity. In this approach, the ER and PR pathway activities in CRC were computed based on the expression profiles of the signature genes of ER and PR pathways, respectively. The results showed that the ER pathway activity was progressively significantly decreased from normal colorectal mucosa, colorectal adenoma to CRC. ER pathway signaling was a favorable factor for the presence of microsatellite stability (MSS) in CRC in seven cohorts tested, while was an unfavorable factor for cancer recurrence in all four CRC cohorts tested (n = 1122; overall HR: 0.311, 95% CI: 0.199-0.488, P < 0.001). Subset stratification in stage II patients showed that ER pathway remained significantly inversely associated with recurrence. PR pathway was also suppressed in colorectal tumors and inversely associated with recurrence of CRC, but to a much lesser extent than ER pathway. Moreover, the inverse association of PR pathway with cancer recurrence was more likely observed in CRC with high ER pathway activity, suggesting the interactions between the two pathways. PR pathway was not associated with MSS in CRC, but it was more significant than ER pathway associated with advance cancer stages and cancer response to adjuvant chemotherapy. These results suggested the potential application of the gene signatures of ER and PR pathways, especially the former, as novel markers for prognosis and management of CRC.

Psychiatric blood biomarkers: avoiding jumping to premature negative or positive conclusions

Blood biomarkers may provide a scientifically useful and clinically usable peripheral signal in psychiatry, as they have been doing for other fields of medicine. Jumping to premature conclusions, negative or positive, can create confusion in this field. Reproducibility is a hallmark of good science. We discuss some recent examples from this dynamic field, and show some new data in support of previously published biomarkers for suicidality (SAT1, MARCKS and SKA2). Methodological clarity and rigor in terms of biomarker discovery, validation and testing is needed. We propose a set of principles for what constitutes a good biomarker, similar in spirit to the Koch postulates used at the birth of the field of infectious diseases.

Genomic classifier ColoPrint predicts recurrence in stage II colorectal cancer patients more accurately than clinical factors

BACKGROUND

Approximately 20% of patients with stage II colorectal cancer will experience a relapse. Current clinical-pathologic stratification factors do not allow clear identification of these high-risk patients. ColoPrint (Agendia, Amsterdam, The Netherlands, http://www.agendia.com) is a gene expression classifier that distinguishes patients with low or high risk of disease relapse.

METHODS

ColoPrint was developed using whole-genome expression data and validated in several independent validation cohorts. Stage II patients from these studies were pooled (n = 416), and ColoPrint was compared with clinical risk factors described in the National Comprehensive Cancer Network (NCCN) 2013 Guidelines for Colon Cancer. Median follow-up was 81 months. Most patients (70%) did not receive adjuvant chemotherapy. Risk of relapse (ROR) was defined as survival until first event of recurrence or death from cancer.

RESULTS

In the pooled stage II data set, ColoPrint identified 63% of patients as low risk with a 5-year ROR of 10%, whereas high-risk patients (37%) had a 5-year ROR of 21%, with a hazard ratio (HR) of 2.16 (p = .004). This remained significant in a multivariate model that included number of lymph nodes retrieved and microsatellite instability. In the T3 microsatellite-stable subgroup (n = 301), ColoPrint classified 59% of patients as low risk with a 5-year ROR of 9.9%. High-risk patients (31%) had a 22.4% ROR (HR: 2.41; p = .005). In contrast, the NCCN clinical high-risk factors were unable to distinguish high- and low-risk patients (15% vs. 13% ROR; p = .55).

CONCLUSION

ColoPrint significantly improved prognostic accuracy independent of microsatellite status or clinical variables, facilitating the identification of patients at higher risk who might be considered for additional treatment.

Significant association of oncogene YAP1 with poor prognosis and cetuximab resistance in colorectal cancer patients

PURPOSE

Activation of YAP1, a novel oncogene in the Hippo pathway, has been observed in many cancers, including colorectal cancer. We investigated whether activation of YAP1 is significantly associated with prognosis or treatment outcomes in colorectal cancer.

EXPERIMENTAL DESIGN

A gene expression signature reflecting YAP1 activation was identified in colorectal cancer cells, and patients with colorectal cancer were stratified into two groups according to this signature: activated YAP1 colorectal cancer (AYCC) or inactivated YAP1 colorectal cancer (IYCC). Stratified patients in five test cohorts were evaluated to determine the effect of the signature on colorectal cancer prognosis and response to cetuximab treatment.

RESULTS

The activated YAP1 signature was associated with poor prognosis for colorectal cancer in four independent patient cohorts with stage I-III disease (total n = 1,028). In a multivariate analysis, the impact of the YAP1 signature on disease-free survival was independent of other clinical variables [hazard ratio (HR), 1.63; 95% confidence interval (CI), 1.25-2.13; P < 0.001]. In patients with stage IV colorectal cancer and wild-type KRAS, IYCC patients had a better disease control rate and progression-free survival (PFS) after cetuximab monotherapy than did AYCC patients; however, in patients with KRAS mutations, PFS duration after cetuximab monotherapy was not different between IYCC and AYCC patients. In multivariate analysis, the effect of YAP1 activation on PFS was independent of KRAS mutation status and other clinical variables (HR, 1.82; 95% CI, 1.05-3.16; P = 0.03).

CONCLUSIONS

Activation of YAP1 is highly associated with poor prognosis for colorectal cancer and may be useful in identifying patients with metastatic colorectal cancer resistant to cetuximab.

Gene expression profiling-derived immunohistochemistry signature with high prognostic value in colorectal carcinoma

OBJECTIVE

Gene expression profiling provides an opportunity to develop robust prognostic markers of colorectal carcinoma (CRC). However, the markers have not been applied for clinical decision making. We aimed to develop an immunohistochemistry signature using microarray data for predicting CRC prognosis.

DESIGN

We evaluated 25 CRC gene signatures in independent microarray datasets with prognosis information and constructed a subnetwork using signatures with high concordance and repeatable prognostic values. Tumours were examined immunohistochemically for the expression of network-centric and the top overlapping molecules. Prognostic values were assessed in 682 patients from Shanghai, China (training cohort) and validated in 343 patients from Guangzhou, China (validation cohort). Median follow-up duration was 58 months. All p values are two-sided.

RESULTS

Five signatures were selected to construct a subnetwork. The expression of GRB2, PTPN11, ITGB1 and POSTN in cancer cells, each significantly associated with disease-free survival, were selected to construct an immunohistochemistry signature. Patients were dichotomised into high-risk and low-risk subgroups with an optimal risk score (1.55). Compared with low-risk patients, high-risk patients had shorter disease-specific survival (DSS) in the training (HR=6.62; 95% CI 3.70 to 11.85) and validation cohorts (HR=3.53; 95% CI 2.13 to 5.84) in multivariate Cox analyses. The signature better predicted DSS than did tumour-node-metastasis staging in both cohorts. In those who received postoperative chemotherapy, high-risk score predicted shorter DSS in the training (HR=6.35; 95% CI 3.55 to 11.36) and validation cohorts (HR=5.56; 95% CI 2.25 to 13.71).

CONCLUSIONS

Our immunohistochemistry signature may be clinically practical for personalised prediction of CRC prognosis.

A nineteen gene-based risk score classifier predicts prognosis of colorectal cancer patients

Colorectal cancer (CRC) patients frequently experience disease recurrence and distant metastasis. This study aimed to identify prognostic indicators, including individual responses to chemotherapy, in CRC patients. RNA-seq data was generated using 54 samples (normal colon, primary CRC, and liver metastases) from 18 CRC patients and genes associated with CRC aggressiveness were identified. A risk score based on these genes was developed and validated in four independent CRC patient cohorts (n = 1063). Diverse statistical methods were applied to validate the risk scoring system, including a generalized linear model likelihood ratio test, Kaplan-Meier curves, a log-rank test, and the Cox model. TREM1 and CTGF were identified as two activated regulators associated with CRC aggressiveness. A risk score based on 19 genes regulated by TREM1 or CTGF activation (TCA19) was a significant prognostic indicator. In multivariate and subset analyses based on pathological staging, TCA19 was an independent risk factor (HR = 1.894, 95% CI = 1.227-2.809, P = 0.002). Subset stratification in stage III patients revealed that TCA19 had prognostic potential and identified patients who would benefit from adjuvant chemotherapy, regardless of age. The TCA19 predictor represents a novel diagnostic tool for identifying high-risk CRC patients and possibly predicting the response to adjuvant chemotherapy.

Co-development of diagnostic vectors to support targeted therapies and theranostics: essential tools in personalized cancer therapy

Novel technologies are being developed to improve patient therapy through the identification of targets and surrogate molecular signatures that can help direct appropriate treatment regimens for efficacy and drug safety. This is particularly the case in oncology whereby patient tumor and biofluids are routinely isolated and analyzed for genetic, immunohistochemical, and/or soluble markers to determine if a predictive biomarker signature (i.e., mutated gene product, differentially expressed protein, altered cell surface antigen, etc.) exists as a means for selecting optimal treatment. These biomarkers may be drug-specific targets and/or differentially expressed nucleic acids, proteins, or cell lineage profiles that can directly affect the patient’s disease tissue or immune response to a therapeutic regimen. Improvements in diagnostics that can prescreen predictive response biomarker profiles will continue to optimize the ability to enhance patient therapy via molecularly defined disease-specific treatment. Conversely, patients lacking predictive response biomarkers will no longer needlessly be exposed to drugs that are unlikely to provide clinical benefit, thereby enabling patients to pursue other therapeutic options and lowering overall healthcare costs by avoiding futile treatment. While patient molecular profiling offers a powerful tool to direct treatment options, the difficulty in identifying disease-specific targets or predictive biomarker signatures that stratify a significant fraction within a disease indication remains challenging. A goal for drug developers is to identify and implement new strategies that can rapidly enable the development of beneficial disease-specific therapies for broad patient-specific targeting without the need of tedious predictive biomarker discovery and validation efforts, currently a bottleneck for development timelines. Successful strategies may gain an advantage by employing repurposed, less-expensive existing agents while potentially improving the therapeutic activity of novel, target-specific therapies that may otherwise have off-target toxicities or less efficacy in cells exhibiting certain pathways. Here, we discuss the use of co-developing diagnostic-targeting vectors to identify patients whose malignant tissue can specifically uptake a targeted anti-cancer drug vector prior to treatment. Using this system, a patient can be predetermined in real-time as to whether or not their tumor(s) can specifically uptake a drug-linked diagnostic vector, thus inferring the uptake of a similar vector linked to an anti-cancer agent. If tumor-specific uptake is observed, then the patient may be suitable for drug-linked vector therapy and have a higher likelihood of clinical benefit while patients with no tumor uptake should consider other therapeutic options. This approach offers complementary opportunities to rapidly develop broad tumor-specific agents for use in personalized medicine.

Proteomic approaches in biomarker discovery: new perspectives in cancer diagnostics

Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies.