Intravascular optical coherence tomography (IVOCT) images are recorded by detecting light backscattered within coronary arteries. We hypothesize that non-thin-capped fibroatheroma (TCFA) causes may scatter light to create the false appearance of IVOCT TCFA. Ten human cadaver hearts were imaged with IVOCT (n=14 coronary arteries). IVOCT and histological TCFA images were coregistered and compared. Of 21 IVOCT TCFAs (fibrous cap <65 μm, lipid arc >1 quadrant), only 8 were true histological TCFA. Foam cell infiltration was responsible for 70% of false IVOCT TCFA and caused both thick-capped fibroatheromas to appear as TCFA, and the appearance of TCFAs when no lipid core was present. Other false IVOCT TCFA causes included smooth muscle cell-rich fibrous tissue (12%) and loose connective tissue (9%). If the lipid arc >1 quadrant (obtuse) criterion was disregarded, 45 IVOCT TCFAs were identified, and sensitivity of IVOCT TCFA detection increased from 63% to 87%, and specificity remained high at 92%. We demonstrate that IVOCT can exhibit 87% (95% CI, 75%-93%) sensitivity and 92% specificity (95% CI, 86%-96%) to detect all lipid arcs (both obtuse and acute, <1 quadrant) TCFA, and we also propose new mechanisms involving light scattering that explain why other plaque components can masquerade as TCFA and cause low positive predictive value of IVOCT for TCFA detection (47% for obtuse lipid arcs). Disregarding the lipid arc >1 quadrant requirement enhances the ability of IVOCT to detect TCFA.

p53 activation is a primary mechanism underlying pathological responses to DNA damaging agents such as chemotherapy and radiotherapy. Our recent animal studies showed that low dose arsenic (LDA)-induced transient p53 inhibition selectively protected normal tissues from chemotherapy-induced toxicity. Study objectives were to: 1) define the lowest safe dose of arsenic trioxide that transiently blocks p53 activation in patients and 2) assess the potential of LDA to decrease hematological toxicity from chemotherapy. Patients scheduled to receive minimum 4 cycles of myelosuppressive chemotherapy were eligible. For objective 1, dose escalation of LDA started at 0.005 mg/kg/day for 3 days. This dose satisfied objective 1 and was administered before chemotherapy cycles 2, 4, and 6 for objective 2. p53 level in peripheral lymphocytes was measured on day 1 of each cycle by ELISA assay. Chemotherapy cycles 1, 3, and 5 served as the baseline for the subsequent cycles of 2, 4, and 6 respectively. If p53 level for the subsequent cycle was lower (or higher) than the baseline cycle, p53 was defined as "suppressed" (or "activated") for the pair of cycles. Repeated measures linear models of CBC in terms of day, cycle, p53 activity and interaction terms were used. Twenty-six patients treated with 3 week cycle regimens form the base of analyses. The mean white blood cell, hemoglobin and absolute neutrophil counts were significantly higher in the "suppressed" relative to the "activated" group. These data support the proof of principle that suppression of p53 could lead to protection of bone marrow in patients receiving chemotherapy. This trial is registered in ClinicalTrials.gov. Identifier: NCT01428128.

Nonrhabdomyosarcoma soft-tissue sarcomas (STSs) are a class of 50+ cancers arising in muscle and soft tissues of children, adolescents, and adults. Rarity of each subtype often precludes subtype-specific preclinical research, leaving many STS patients with limited treatment options should frontline therapy be insufficient. When clinical options are exhausted, personalized therapy assignment approaches may help direct patient care. Here, we report the results of an adult female STS patient with relapsed undifferentiated pleomorphic sarcoma (UPS) who self-drove exploration of a wide array of personalized Clinical Laboratory Improvement Amendments (CLIAs) level and research-level diagnostics, including state of the art genomic, proteomic, ex vivo live cell chemosensitivity testing, a patient-derived xenograft model, and immunoscoring. Her therapeutic choices were also diverse, including neoadjuvant chemotherapy, radiation therapy, and surgeries. Adjuvant and recurrence strategies included off-label and natural medicines, several immunotherapies, and N-of-1 approaches. Identified treatment options, especially those validated during the in vivo study, were not introduced into the course of clinical treatment but did provide plausible treatment regimens based on FDA-approved clinical agents.

Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children and phenocopies a muscle precursor that fails to undergo terminal differentiation. The alveolar subtype (ARMS) has the poorest prognosis and represents the greatest unmet medical need for RMS. Emerging evidence supports the role of epigenetic dysregulation in RMS. Here we show that SMARCA4/BRG1, an ATP-dependent chromatin remodeling enzyme of the SWI/SNF complex, is prominently expressed in primary tumors from ARMS patients and cell cultures. Our validation studies for a CRISPR screen of 400 epigenetic targets identified SMARCA4 as a unique factor for long-term (but not short-term) tumor cell survival in ARMS. A SMARCA4/SMARCA2 protein degrader (ACBI-1) demonstrated similar long-term tumor cell dependence in vitro and in vivo. These results credential SMARCA4 as a tumor cell dependency factor and a therapeutic target in ARMS.

Background

Cancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates molecular sequencing data with functional assay data to develop patient-specific combination cancer treatments.

Methods

Tissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the primary murine tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient's epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient's primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay.

Results

Our computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model).

Conclusions

These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.