Prediction of cancer treatment response from histopathology images through imputed transcriptomics.

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Our key contribution is showing that ENLIGHT-DeepPT successfully predicts true responders in five independent patients' cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, which needs to be trained and tested on the same cohort. ENLIGHT-DeepPT future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries.

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome.

BACKGROUND: Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. METHODS: We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort. FINDINGS: Evaluating ENLIGHT's performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient's treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy. CONCLUSIONS: ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome. FUNDING: This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

Fibrolamellar carcinoma transcriptomic-based treatment prediction: complete response after nivolumab and ipilimumab.

Fibrolamellar carcinoma (FLC) is a rare cancer of the liver that most commonly affects children and young adults. There is no clear standard of care for the disease, whose response to treatment seems to be very different from that of hepatocellular carcinoma. We present a case of FLC in a patient in her mid 30s that recurred and persisted despite resection and multiple lines of treatment. Following transcriptomic analysis, a combination of ipilimumab (anti-CTLA4) and nivolumab (anti-PD-1) led to complete remission, although common biomarkers for immune checkpoint blockade were all negative in this case. The patient is still in remission. Here, combined checkpoint blockade guided by novel transcriptomic analysis led to complete remission after failure of several lines of treatment.

Synthetic lethality-mediated precision oncology via the tumor transcriptome.

Precision oncology has made significant advances, mainly by targeting actionable mutations in cancer driver genes. Aiming to expand treatment opportunities, recent studies have begun to explore the utility of tumor transcriptome to guide patient treatment. Here, we introduce SELECT (synthetic lethality and rescue-mediated precision oncology via the transcriptome), a precision oncology framework harnessing genetic interactions to predict patient response to cancer therapy from the tumor transcriptome. SELECT is tested on a broad collection of 35 published targeted and immunotherapy clinical trials from 10 different cancer types. It is predictive of patients' response in 80% of these clinical trials and in the recent multi-arm WINTHER trial. The predictive signatures and the code are made publicly available for academic use, laying a basis for future prospective clinical studies.

Sexual selection and the evolution of obligatory sex.

BACKGROUND: Among the long-standing conundrums of evolutionary theory, obligatory sex is one of the hardest. Current theory suggests multiple factors that might explain the benefits of sex when compared with complete asexuality, but no satisfactory explanation for the prevalence of obligatory sex in the face of facultative sexual reproduction. RESULTS AND CONCLUSION: We show that when sexual selection is present obligatory sex can evolve and be maintained even against facultative sex, under common scenarios of deleterious mutations and environmental changes.

Why is stress so deadly? An evolutionary perspective.

The reaction of the body to prolonged stress has many harmful effects. Classical theory assumes that stress responses have evolved due to their short-term selective advantages ('flight or fight'), and despite their adverse long-term effects. In contrast, we demonstrate that the adverse effects of stress responses may have a selective advantage. Using an analytical model we show that a gene that causes the early death of a relatively unfit individual can increase in frequency in a structured population even if it has no positive effect on that individual. This result offers a new perspective on the relations between stress factors, stress responses and stress-related diseases.

On the evolutionary advantage of fitness-associated recombination.

The adaptive value of recombination remains something of a puzzle. One of the basic problems is that recombination not only creates new and advantageous genetic combinations, but also breaks down existing good ones. A negative correlation between the fitness of an individual and its recombination rate would result in prolonged integrity of fitter genetic combinations while enabling less fit ones to produce new combinations. Such a correlation could be mediated by various factors, including stress responses, age, or direct DNA damage. For haploid population models, we show that an allele for such fitness-associated recombination (FAR) can spread both in asexual populations and in populations reproducing sexually at any uniform recombination rate. FAR also carries an advantage for the population as a whole, resulting in a higher average fitness at mutation-selection balance. These results are demonstrated in populations adapting to new environments as well as in well-adapted populations coping with deleterious mutations. Current experimental results providing evidence for the existence of FAR in nature are discussed.