Treatment efficacy score: a better surrogate for arm-level survival differences in neoadjuvant breast cancer trials?

Neoadjuvant chemotherapy is widely used in the therapy of stage II-III breast cancers and pathologic complete response (pCR; ypT0/is, ypN0) predicts excellent long-term survival. However, the correlation between improvement in pCR rate and survival is highly variable across trials. A major limitation of pCR is that it does not capture downstaging in patients with residual disease. We previously introduced the residual cancer burden score that measures pathologic response on a continuous scale. Comparison of residual cancer burden score distributions between trial arms reflects treatment efficacy more accurately than differences in pCR rate. We developed the treatment efficacy score as a new statistical metric that appears to be a better surrogate for trial arm-level survival improvement than pCR rate difference.

Network propagation-based prioritization of long tail genes in 17 cancer types

BACKGROUND

The diversity of genomic alterations in cancer poses challenges to fully understanding the etiologies of the disease. Recent interest in infrequent mutations, in genes that reside in the "long tail" of the mutational distribution, uncovered new genes with significant implications in cancer development. The study of cancer-relevant genes often requires integrative approaches pooling together multiple types of biological data. Network propagation methods demonstrate high efficacy in achieving this integration. Yet, the majority of these methods focus their assessment on detecting known cancer genes or identifying altered subnetworks. In this paper, we introduce a network propagation approach that entirely focuses on prioritizing long tail genes with potential functional impact on cancer development.

RESULTS

We identify sets of often overlooked, rarely to moderately mutated genes whose biological interactions significantly propel their mutation-frequency-based rank upwards during propagation in 17 cancer types. We call these sets "upward mobility genes" and hypothesize that their significant rank improvement indicates functional importance. We report new cancer-pathway associations based on upward mobility genes that are not previously identified using driver genes alone, validate their role in cancer cell survival in vitro using extensive genome-wide RNAi and CRISPR data repositories, and further conduct in vitro functional screenings resulting in the validation of 18 previously unreported genes.

CONCLUSION

Our analysis extends the spectrum of cancer-relevant genes and identifies novel potential therapeutic targets.