Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned

Modern approaches to biomedical research and diagnostics targeted towards precision medicine are generating ‘big data’ across a range of high-throughput experimental and analytical platforms. Integrative analysis of this rich clinical, pathological, molecular and imaging data represents one of the greatest bottlenecks in biomarker discovery research in cancer and other diseases. Following on from the publication of our successful framework for multimodal data amalgamation and integrative analysis, Pathology Integromics in Cancer (PICan), this article will explore the essential elements of assembling an integromics framework from a more detailed perspective. PICan, built around a relational database storing curated multimodal data, is the research tool sitting at the heart of our interdisciplinary efforts to streamline biomarker discovery and validation. While recognizing that every institution has a unique set of priorities and challenges, we will use our experiences with PICan as a case study and starting point, rationalizing the design choices we made within the context of our local infrastructure and specific needs, but also highlighting alternative approaches that may better suit other programmes of research and discovery. Along the way, we stress that integromics is not just a set of tools, but rather a cohesive paradigm for how modern bioinformatics can be enhanced. Successful implementation of an integromics framework is a collaborative team effort that is built with an eye to the future and greatly accelerates the processes of biomarker discovery, validation and translation into clinical practice.

Challenging the cancer molecular stratification dogma: Intratumoral heterogeneity undermines consensus molecular subtypes and potential diagnostic value in colorectal cancer

Purpose:
A number of independent gene expression profiling studies have identified transcriptional subtypes in colorectal cancer (CRC) with potential diagnostic utility, culminating in publication of a CRC Consensus Molecular Subtype classification. The worst prognostic subtype has been defined by genes associated with stem-like biology. Recently, it has been shown that the majority of genes associated with this poor prognostic group are stromal-derived. We investigated the potential for tumor misclassification into multiple diagnostic subgroups based on tumoral region sampled.

Experimental Design:
We performed multi-region tissue RNA extraction/transcriptomic analysis using Colorectal Specific Arrays on invasive front, central tumor and lymph node regions selected from tissue samples from 25 CRC patients.

Results:
We identified a consensus 30 gene list which represents the intratumoral heterogeneity within a cohort of primary CRC tumors. Using a series of online datasets, we showed that this gene list displays prognostic potential (HR=2.914 (CI 0.9286-9.162) in stage II/III CRC patients, but in addition we demonstrated that these genes are stromal derived, challenging the assumption that poor prognosis tumors with stem-like biology have undergone a widespread Epithelial Mesenchymal Transition (EMT). Most importantly, we showed that patients can be simultaneously classified into multiple diagnostically relevant subgroups based purely on the tumoral region analysed.

Conclusions:
Gene expression profiles derived from the non-malignant stromal region can influence assignment of CRC transcriptional subtypes, questioning the current molecular classification dogma and highlighting the need to consider pathology sampling region and degree of stromal infiltration when employing transcription-based classifiers to underpin clinical decision-making in CRC.

Immune-derived PD-L1 gene expression defines a subgroup of stage II/III colorectal cancer patients with favorable prognosis that may be harmed by adjuvant chemotherapy

A recent phase 2 study of metastatic colorectal carcinoma (CRC) patients showed that mismatch repair gene status was predictive of clinical response to PD-1-targeting immune checkpoint blockade. Further examination revealed strong correlation between PD-L1 protein expression and microsatellite instability (MSI) in stage IV CRC, suggesting that the amount of PD-L1 protein expression could identify late stage patients who may benefit from immunotherapy. To assess whether the clinical associations between PD-L1 gene expression and MSI identified in metastatic CRC are also present in stage II/III CRC, we used in silico analysis to elucidate the cell types expressing the PD-L1 gene. We found a significant association of PD-L1 gene expression with MSI in early stage CRC (P < 0.001) and show that unlike in non-CRC tumors, PD-L1 is derived predominantly from the immune infiltrate. We demonstrate that PD-L1 gene expression has positive prognostic value in the adjuvant disease setting (PD-L1low v PD-L1high HR = 9.09; CI, 2.11-39.10). PD-L1 gene expression had predictive value, as patients with high PD-L1 expression appear to be harmed by standard-of-care treatment (HR = 4.95; CI,1.10-22.35). Building on the promising results from the metastatic CRC PD-1-targeting trial, we provide compelling evidence that PD-L1high/MSI/immunehigh stage II/III CRC patients should not receive standard chemotherapy. This conclusion supports the rationale to clinically evaluate this patient subgroup for PD-1 blockade treatment.