Cell Cycle News & Views to:
AK Witkiewicz, D Whitaker-Menezes, A Dasgupta, NJ Philp, Z Lin, R Gandara, S Sneddon, UE Martinez-Outschoorn, F Sotgia, MP Lisanti. Using the 'reverse Warburg effect' to identify high-risk breast cancer patients: Stromal MCT4 predicts poor clinical outcome in triple-negative breast cancers. Cell Cycle 2012; 11: 1108-17
PMID: 22313602 DOI: 10.4161/cc.11.6.19530
Received: March 19, 2012; Accepted: March 20, 2012
Cancer is characterized by somatic mutations that provide a growth and survival advantage. The mutations are a function of chance because of the random nature of mutagenesis. However, the biology in the tumor cells drives the selection of mutations that are advantageous to the cancer.
Triple-negative breast cancers are estrogen and progesterone receptor-negative and HER-2-negative and account for 10–20% of all breast cancers.
Prognostic biomarkers do not have to be limited to the tumor cell. The ratio of tumor to stroma in TNBC is a predictor of outcome; tumors with < 50% stroma have a 5‑year progression-free survival and overall survival of 85% and 89% compared with 45% and 65% in tumors with > 50% stroma.
MCT4 is a plausible therapeutic target whether considering excessive production of lactate by stromal tissue or tumor cells. In either scenario, the metabolism of the tumor would be disturbed, causing intracellular acidosis if MCT4 is on the tumor cells and starving the tumor if MCT4 is on the stromal cells. Would patients with TNBC with Cav-1 low/MCT4 high stromal tissue elicit a clinical benefit from decreasing the availability of lactate? Possibly. However, the tumor metabolism might adapt to use alternative sources of carbon. On the upside, the acidosis in the MCT4-positive stromal cells might kill the stromal cells, lowering the stroma/tumor ratio and subsequently extending relapse-free survival.
The characterization of the cancer-stromal relationship is gaining momentum, from direct interactions, e.g., through integrins, to signaling molecules such as VEGF..
In conclusion, there is crosstalk at multiple levels between tumor cells and the stroma, providing a rationale to target the stroma. This is unlikely to be a panacea for treating cancer as shown by the inter- and intra-tumor-type heterogeneity that has been shown for tumor-stroma interactions. What will predict which tumors will respond to stroma directed therapies; the biology of the stroma, the biology and genetics of the tumor or both? As the volume of cancer genome data grows, we would be wise to consider how the genetics of tumors is molded by the microenvironment and vice versa, and how we might target the microenvironment for clinical benefit.

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