Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here, we perform a genome-wide in silico prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients' response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.

Targeted BRAF inhibition (BRAFi) and combined BRAF and MEK inhibition (BRAFi and MEKi) therapies have markedly improved the clinical outcomes of patients with metastatic melanoma. Unfortunately, the efficacy of these treatments is often countered by the acquisition of drug resistance. Here we investigated the molecular mechanisms that underlie acquired resistance to BRAFi and to the combined therapy. Consistent with previous studies, we show that resistance to BRAFi is mediated by ERK pathway reactivation. Resistance to the combined therapy, however, is mediated by mechanisms independent of reactivation of ERK in many resistant cell lines and clinical samples. p21-activated kinases (PAKs) become activated in cells with acquired drug resistance and have a pivotal role in mediating resistance. Our screening, using a reverse-phase protein array, revealed distinct mechanisms by which PAKs mediate resistance to BRAFi and the combined therapy. In BRAFi-resistant cells, PAKs phosphorylate CRAF and MEK to reactivate ERK. In cells that are resistant to the combined therapy, PAKs regulate JNK and β-catenin phosphorylation and mTOR pathway activation, and inhibit apoptosis, thereby bypassing ERK. Together, our results provide insights into the molecular mechanisms underlying acquired drug resistance to current targeted therapies, and may help to direct novel drug development efforts to overcome acquired drug resistance.

Immune checkpoint inhibitors (ICIs) produce durable responses in some melanoma patients, but many patients derive no clinical benefit, and the molecular underpinnings of such resistance remain elusive. Here, we leveraged single-cell RNA sequencing (scRNA-seq) from 33 melanoma tumors and computational analyses to interrogate malignant cell states that promote immune evasion. We identified a resistance program expressed by malignant cells that is associated with T cell exclusion and immune evasion. The program is expressed prior to immunotherapy, characterizes cold niches in situ, and predicts clinical responses to anti-PD-1 therapy in an independent cohort of 112 melanoma patients. CDK4/6-inhibition represses this program in individual malignant cells, induces senescence, and reduces melanoma tumor outgrowth in mouse models in vivo when given in combination with immunotherapy. Our study provides a high-resolution landscape of ICI-resistant cell states, identifies clinically predictive signatures, and suggests new therapeutic strategies to overcome immunotherapy resistance.

Although the pattern of BAP1 inactivation in ocular melanoma specimens and in the BAP1 cutaneous melanoma (CM)/ocular melanoma predisposition syndrome suggests a tumor suppressor function, the specific role of this gene in the pathogenesis of CM is not fully understood. We thus set out to characterize BAP1 in CM and discovered an unexpected pro-survival effect of this protein. Tissue and cell lines analysis showed that BAP1 expression was maintained, rather than lost, in primary melanomas compared with nevi and normal skin. Genetic depletion of BAP1 in melanoma cells reduced proliferation and colony-forming capability, induced apoptosis, and inhibited melanoma tumor growth in vivo. On the molecular level, suppression of BAP1 led to a concomitant drop in the protein levels of survivin, a member of anti-apoptotic proteins and a known mediator of melanoma survival. Restoration of survivin in melanoma cells partially rescued the growth-retarding effects of BAP1 loss. In contrast to melanoma cells, stable overexpression of BAP1 into immortalized but non-transformed melanocytes did suppress proliferation and reduce survivin. Taken together, these studies demonstrate that BAP1 may have a growth-sustaining role in melanoma cells, but that its impact on ubiquitination underpins a complex physiology, which is context and cell dependent.

Response to targeted therapies varies significantly despite shared oncogenic mutations. Nowhere is this more apparent than in BRAF (V600E)-mutated melanomas where initial drug response can be striking and yet relapse is commonplace. Resistance to BRAF inhibitors have been attributed to the activation of various receptor tyrosine kinases (RTKs), although the underlying mechanisms have been largely uncharacterized. Here, we found that EGFR-induced vemurafenib resistance is ligand dependent. We employed whole-genome expression analysis and discovered that vemurafenib resistance correlated with the loss of microphthalmia-associated transcription factor (MITF), along with its melanocyte lineage program, and with the activation of EGFR signaling. An inverse relationship between MITF, vemurafenib resistance, and EGFR was then observed in patient samples of recurrent melanoma and was conserved across melanoma cell lines and patients' tumor specimens. Functional studies revealed that MITF depletion activated EGFR signaling and consequently recapitulated the resistance phenotype. In contrast, forced expression of MITF in melanoma and colon cancer cells inhibited EGFR and conferred sensitivity to BRAF/MEK inhibitors. These findings indicate that an "autocrine drug resistance loop" is suppressed by melanocyte lineage signal(s), such as MITF. This resistance loop modulates drug response and could explain the unique sensitivity of melanomas to BRAF inhibition.

High content neuron image processing is considered as an important method for quantitative neurobiological studies. The main goal of analysis in this paper is to provide automatic image processing approaches to process neuron images for studying neuron mechanism in high content screening. In the nuclei channel, all nuclei are segmented and detected by applying the gradient vector field based watershed. Then the neuronal nuclei are selected based on the soma region detected in neurite channel. In neurite images, we propose a novel neurite centerline extraction approach using the improved line-pixel detection technique. The proposed neurite tracing method can detect the curvilinear structure more accurately compared with the current existing methods. An interface called NeuriteIQ based on the proposed algorithms is developed finally for better application in high content screening.

Both genomic and transcriptomic signatures have been developed to predict responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies; however, most of these signatures are derived from pre-treatment biopsy samples. Here, we build pathway-based super signatures in pre-treatment (PASS-PRE) and on-treatment (PASS-ON) tumor specimens based on transcriptomic data and clinical information from a large dataset of metastatic melanoma treated with anti-PD1-based therapies as the training set. Both PASS-PRE and PASS-ON signatures are validated in three independent datasets of metastatic melanoma as the validation set, achieving area under the curve (AUC) values of 0.45-0.69 and 0.85-0.89, respectively. We also combine all test samples and obtain AUCs of 0.65 and 0.88 for PASS-PRE and PASS-ON signatures, respectively. When compared with existing signatures, the PASS-ON signature demonstrates more robust and superior predictive performance across all four datasets. Overall, we provide a framework for building pathway-based signatures that is highly and accurately predictive of response to anti-PD1 therapies based on on-treatment tumor specimens. This work would provide a rationale for applying pathway-based signatures derived from on-treatment tumor samples to predict patients' therapeutic response to ICB therapies.